Overview

Dataset statistics

Number of variables41
Number of observations79672
Missing cells43336
Missing cells (%)1.3%
Duplicate rows11
Duplicate rows (%)< 0.1%
Total size in memory24.9 MiB
Average record size in memory328.0 B

Variable types

Categorical26
Numeric15

Alerts

count has constant value "1" Constant
Dataset has 11 (< 0.1%) duplicate rowsDuplicates
animal_id_outcome has a high cardinality: 71961 distinct values High cardinality
date_of_birth has a high cardinality: 5923 distinct values High cardinality
outcome_datetime has a high cardinality: 65686 distinct values High cardinality
outcome_monthyear has a high cardinality: 55 distinct values High cardinality
dob_monthyear has a high cardinality: 55 distinct values High cardinality
animal_id_intake has a high cardinality: 71961 distinct values High cardinality
breed has a high cardinality: 2155 distinct values High cardinality
color has a high cardinality: 529 distinct values High cardinality
found_location has a high cardinality: 36576 distinct values High cardinality
intake_datetime has a high cardinality: 56747 distinct values High cardinality
intake_monthyear has a high cardinality: 54 distinct values High cardinality
time_in_shelter has a high cardinality: 29319 distinct values High cardinality
age_upon_outcome_(days) is highly correlated with age_upon_outcome_(years) and 3 other fieldsHigh correlation
age_upon_outcome_(years) is highly correlated with age_upon_outcome_(days) and 3 other fieldsHigh correlation
outcome_month is highly correlated with intake_monthHigh correlation
outcome_year is highly correlated with dob_year and 1 other fieldsHigh correlation
outcome_number is highly correlated with intake_numberHigh correlation
dob_year is highly correlated with age_upon_outcome_(days) and 5 other fieldsHigh correlation
dob_month is highly correlated with intake_monthHigh correlation
age_upon_intake_(days) is highly correlated with age_upon_outcome_(days) and 3 other fieldsHigh correlation
age_upon_intake_(years) is highly correlated with age_upon_outcome_(days) and 3 other fieldsHigh correlation
intake_month is highly correlated with outcome_month and 1 other fieldsHigh correlation
intake_year is highly correlated with outcome_year and 1 other fieldsHigh correlation
intake_number is highly correlated with outcome_numberHigh correlation
age_upon_outcome_(days) is highly correlated with age_upon_outcome_(years) and 3 other fieldsHigh correlation
age_upon_outcome_(years) is highly correlated with age_upon_outcome_(days) and 3 other fieldsHigh correlation
outcome_month is highly correlated with intake_monthHigh correlation
outcome_year is highly correlated with intake_yearHigh correlation
outcome_number is highly correlated with intake_numberHigh correlation
dob_year is highly correlated with age_upon_outcome_(days) and 3 other fieldsHigh correlation
dob_month is highly correlated with intake_monthHigh correlation
age_upon_intake_(days) is highly correlated with age_upon_outcome_(days) and 3 other fieldsHigh correlation
age_upon_intake_(years) is highly correlated with age_upon_outcome_(days) and 3 other fieldsHigh correlation
intake_month is highly correlated with outcome_month and 1 other fieldsHigh correlation
intake_year is highly correlated with outcome_yearHigh correlation
intake_number is highly correlated with outcome_numberHigh correlation
age_upon_outcome_(days) is highly correlated with age_upon_outcome_(years) and 3 other fieldsHigh correlation
age_upon_outcome_(years) is highly correlated with age_upon_outcome_(days) and 3 other fieldsHigh correlation
outcome_month is highly correlated with intake_monthHigh correlation
outcome_year is highly correlated with intake_yearHigh correlation
outcome_number is highly correlated with intake_numberHigh correlation
dob_year is highly correlated with age_upon_outcome_(days) and 3 other fieldsHigh correlation
age_upon_intake_(days) is highly correlated with age_upon_outcome_(days) and 3 other fieldsHigh correlation
age_upon_intake_(years) is highly correlated with age_upon_outcome_(days) and 3 other fieldsHigh correlation
intake_month is highly correlated with outcome_monthHigh correlation
intake_year is highly correlated with outcome_yearHigh correlation
intake_number is highly correlated with outcome_numberHigh correlation
intake_weekday is highly correlated with countHigh correlation
intake_type is highly correlated with count and 1 other fieldsHigh correlation
outcome_type is highly correlated with outcome_subtype and 1 other fieldsHigh correlation
age_upon_outcome is highly correlated with age_upon_intake_age_group and 3 other fieldsHigh correlation
outcome_subtype is highly correlated with outcome_type and 2 other fieldsHigh correlation
sex_upon_outcome is highly correlated with sex_upon_intake and 1 other fieldsHigh correlation
outcome_monthyear is highly correlated with dob_monthyear and 2 other fieldsHigh correlation
dob_monthyear is highly correlated with outcome_monthyear and 2 other fieldsHigh correlation
intake_condition is highly correlated with countHigh correlation
sex_upon_intake is highly correlated with sex_upon_outcome and 1 other fieldsHigh correlation
age_upon_intake_age_group is highly correlated with age_upon_outcome and 3 other fieldsHigh correlation
intake_monthyear is highly correlated with outcome_monthyear and 2 other fieldsHigh correlation
age_upon_intake is highly correlated with age_upon_outcome and 3 other fieldsHigh correlation
count is highly correlated with intake_weekday and 15 other fieldsHigh correlation
outcome_weekday is highly correlated with countHigh correlation
animal_type is highly correlated with intake_type and 2 other fieldsHigh correlation
age_upon_outcome_age_group is highly correlated with age_upon_outcome and 3 other fieldsHigh correlation
age_upon_outcome is highly correlated with outcome_subtype and 12 other fieldsHigh correlation
outcome_subtype is highly correlated with age_upon_outcome and 7 other fieldsHigh correlation
outcome_type is highly correlated with age_upon_outcome and 5 other fieldsHigh correlation
sex_upon_outcome is highly correlated with age_upon_outcome and 4 other fieldsHigh correlation
age_upon_outcome_(days) is highly correlated with age_upon_outcome and 8 other fieldsHigh correlation
age_upon_outcome_(years) is highly correlated with age_upon_outcome and 8 other fieldsHigh correlation
age_upon_outcome_age_group is highly correlated with age_upon_outcome and 8 other fieldsHigh correlation
outcome_month is highly correlated with outcome_monthyear and 4 other fieldsHigh correlation
outcome_year is highly correlated with outcome_monthyear and 4 other fieldsHigh correlation
outcome_monthyear is highly correlated with outcome_month and 7 other fieldsHigh correlation
outcome_hour is highly correlated with outcome_subtypeHigh correlation
outcome_number is highly correlated with intake_numberHigh correlation
dob_year is highly correlated with age_upon_outcome and 12 other fieldsHigh correlation
dob_month is highly correlated with outcome_month and 4 other fieldsHigh correlation
dob_monthyear is highly correlated with outcome_month and 7 other fieldsHigh correlation
age_upon_intake is highly correlated with age_upon_outcome and 10 other fieldsHigh correlation
animal_type is highly correlated with age_upon_outcome and 4 other fieldsHigh correlation
intake_type is highly correlated with outcome_subtype and 4 other fieldsHigh correlation
sex_upon_intake is highly correlated with age_upon_outcome and 11 other fieldsHigh correlation
age_upon_intake_(days) is highly correlated with age_upon_outcome and 8 other fieldsHigh correlation
age_upon_intake_(years) is highly correlated with age_upon_outcome and 8 other fieldsHigh correlation
age_upon_intake_age_group is highly correlated with age_upon_outcome and 8 other fieldsHigh correlation
intake_month is highly correlated with outcome_month and 4 other fieldsHigh correlation
intake_year is highly correlated with outcome_year and 4 other fieldsHigh correlation
intake_monthyear is highly correlated with outcome_month and 7 other fieldsHigh correlation
intake_number is highly correlated with outcome_numberHigh correlation
outcome_subtype has 43324 (54.4%) missing values Missing
animal_id_outcome is uniformly distributed Uniform
outcome_datetime is uniformly distributed Uniform
animal_id_intake is uniformly distributed Uniform
outcome_hour has 2044 (2.6%) zeros Zeros

Reproduction

Analysis started2023-03-14 02:16:56.244315
Analysis finished2023-03-14 02:18:27.461084
Duration1 minute and 31.22 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

age_upon_outcome
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct46
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size622.6 KiB
1 year
14750 
2 years
11542 
2 months
9246 
3 years
5277 
3 months
 
3401
Other values (41)
35456 

Length

Max length9
Median length8
Mean length7.154370419
Min length5

Characters and Unicode

Total characters570003
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row10 years
2nd row7 years
3rd row6 years
4th row10 years
5th row16 years

Common Values

ValueCountFrequency (%)
1 year14750
18.5%
2 years11542
14.5%
2 months9246
11.6%
3 years5277
 
6.6%
3 months3401
 
4.3%
1 month3396
 
4.3%
4 years3044
 
3.8%
5 years2747
 
3.4%
4 months2417
 
3.0%
5 months1955
 
2.5%
Other values (36)21897
27.5%

Length

2023-03-14T02:18:27.599771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
years31896
20.0%
months23566
14.8%
222354
14.0%
119256
12.1%
year14750
9.3%
310390
 
6.5%
46802
 
4.3%
54885
 
3.1%
weeks4612
 
2.9%
63907
 
2.5%
Other values (21)16926
10.6%

Most occurring characters

ValueCountFrequency (%)
79672
14.0%
s60942
10.7%
e56724
10.0%
y47671
 
8.4%
a47671
 
8.4%
r46646
 
8.2%
h26962
 
4.7%
t26962
 
4.7%
m26962
 
4.7%
o26962
 
4.7%
Other values (14)122829
21.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter405567
71.2%
Decimal Number84764
 
14.9%
Space Separator79672
 
14.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s60942
15.0%
e56724
14.0%
y47671
11.8%
a47671
11.8%
r46646
11.5%
h26962
6.6%
t26962
6.6%
m26962
6.6%
o26962
6.6%
n26962
6.6%
Other values (3)11103
 
2.7%
Decimal Number
ValueCountFrequency (%)
125286
29.8%
222989
27.1%
310789
12.7%
47057
 
8.3%
55100
 
6.0%
64014
 
4.7%
82817
 
3.3%
72618
 
3.1%
02537
 
3.0%
91557
 
1.8%
Space Separator
ValueCountFrequency (%)
79672
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin405567
71.2%
Common164436
28.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
s60942
15.0%
e56724
14.0%
y47671
11.8%
a47671
11.8%
r46646
11.5%
h26962
6.6%
t26962
6.6%
m26962
6.6%
o26962
6.6%
n26962
6.6%
Other values (3)11103
 
2.7%
Common
ValueCountFrequency (%)
79672
48.5%
125286
 
15.4%
222989
 
14.0%
310789
 
6.6%
47057
 
4.3%
55100
 
3.1%
64014
 
2.4%
82817
 
1.7%
72618
 
1.6%
02537
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII570003
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
79672
14.0%
s60942
10.7%
e56724
10.0%
y47671
 
8.4%
a47671
 
8.4%
r46646
 
8.2%
h26962
 
4.7%
t26962
 
4.7%
m26962
 
4.7%
o26962
 
4.7%
Other values (14)122829
21.5%

animal_id_outcome
Categorical

HIGH CARDINALITY
UNIFORM

Distinct71961
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Memory size622.6 KiB
A721033
 
13
A718223
 
11
A706536
 
11
A694501
 
8
A716018
 
8
Other values (71956)
79621 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters557704
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65890 ?
Unique (%)82.7%

Sample

1st rowA006100
2nd rowA006100
3rd rowA006100
4th rowA047759
5th rowA134067

Common Values

ValueCountFrequency (%)
A72103313
 
< 0.1%
A71822311
 
< 0.1%
A70653611
 
< 0.1%
A6945018
 
< 0.1%
A7160188
 
< 0.1%
A7383248
 
< 0.1%
A6164448
 
< 0.1%
A7356017
 
< 0.1%
A6782947
 
< 0.1%
A5935377
 
< 0.1%
Other values (71951)79584
99.9%

Length

2023-03-14T02:18:27.785260image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a72103313
 
< 0.1%
a70653611
 
< 0.1%
a71822311
 
< 0.1%
a6945018
 
< 0.1%
a7160188
 
< 0.1%
a7383248
 
< 0.1%
a6164448
 
< 0.1%
a7380737
 
< 0.1%
a6831087
 
< 0.1%
a7019017
 
< 0.1%
Other values (71951)79584
99.9%

Most occurring characters

ValueCountFrequency (%)
788898
15.9%
A79672
14.3%
670578
12.7%
541173
7.4%
340062
7.2%
839800
7.1%
039734
7.1%
139721
7.1%
439721
7.1%
239392
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number478032
85.7%
Uppercase Letter79672
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
788898
18.6%
670578
14.8%
541173
8.6%
340062
8.4%
839800
8.3%
039734
8.3%
139721
8.3%
439721
8.3%
239392
8.2%
938953
8.1%
Uppercase Letter
ValueCountFrequency (%)
A79672
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common478032
85.7%
Latin79672
 
14.3%

Most frequent character per script

Common
ValueCountFrequency (%)
788898
18.6%
670578
14.8%
541173
8.6%
340062
8.4%
839800
8.3%
039734
8.3%
139721
8.3%
439721
8.3%
239392
8.2%
938953
8.1%
Latin
ValueCountFrequency (%)
A79672
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII557704
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
788898
15.9%
A79672
14.3%
670578
12.7%
541173
7.4%
340062
7.2%
839800
7.1%
039734
7.1%
139721
7.1%
439721
7.1%
239392
7.1%

date_of_birth
Categorical

HIGH CARDINALITY

Distinct5923
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size622.6 KiB
2014-05-05 00:00:00
 
112
2015-09-01 00:00:00
 
112
2014-04-21 00:00:00
 
106
2015-04-28 00:00:00
 
104
2016-05-01 00:00:00
 
103
Other values (5918)
79135 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1513768
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1027 ?
Unique (%)1.3%

Sample

1st row2007-07-09 00:00:00
2nd row2007-07-09 00:00:00
3rd row2007-07-09 00:00:00
4th row2004-04-02 00:00:00
5th row1997-10-16 00:00:00

Common Values

ValueCountFrequency (%)
2014-05-05 00:00:00112
 
0.1%
2015-09-01 00:00:00112
 
0.1%
2014-04-21 00:00:00106
 
0.1%
2015-04-28 00:00:00104
 
0.1%
2016-05-01 00:00:00103
 
0.1%
2015-04-20 00:00:00100
 
0.1%
2015-04-27 00:00:0090
 
0.1%
2016-04-15 00:00:0090
 
0.1%
2015-04-19 00:00:0090
 
0.1%
2014-05-02 00:00:0082
 
0.1%
Other values (5913)78683
98.8%

Length

2023-03-14T02:18:27.953826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:0079672
50.0%
2014-05-05112
 
0.1%
2015-09-01112
 
0.1%
2014-04-21106
 
0.1%
2015-04-28104
 
0.1%
2016-05-01103
 
0.1%
2015-04-20100
 
0.1%
2015-04-2790
 
0.1%
2016-04-1590
 
0.1%
2015-04-1990
 
0.1%
Other values (5914)78765
49.4%

Most occurring characters

ValueCountFrequency (%)
0669507
44.2%
-159344
 
10.5%
:159344
 
10.5%
1139048
 
9.2%
2130934
 
8.6%
79672
 
5.3%
431837
 
2.1%
531344
 
2.1%
330798
 
2.0%
626507
 
1.8%
Other values (3)55433
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1115408
73.7%
Dash Punctuation159344
 
10.5%
Other Punctuation159344
 
10.5%
Space Separator79672
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0669507
60.0%
1139048
 
12.5%
2130934
 
11.7%
431837
 
2.9%
531344
 
2.8%
330798
 
2.8%
626507
 
2.4%
722670
 
2.0%
916469
 
1.5%
816294
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
-159344
100.0%
Other Punctuation
ValueCountFrequency (%)
:159344
100.0%
Space Separator
ValueCountFrequency (%)
79672
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1513768
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0669507
44.2%
-159344
 
10.5%
:159344
 
10.5%
1139048
 
9.2%
2130934
 
8.6%
79672
 
5.3%
431837
 
2.1%
531344
 
2.1%
330798
 
2.0%
626507
 
1.8%
Other values (3)55433
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1513768
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0669507
44.2%
-159344
 
10.5%
:159344
 
10.5%
1139048
 
9.2%
2130934
 
8.6%
79672
 
5.3%
431837
 
2.1%
531344
 
2.1%
330798
 
2.0%
626507
 
1.8%
Other values (3)55433
 
3.7%

outcome_subtype
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct19
Distinct (%)0.1%
Missing43324
Missing (%)54.4%
Memory size622.6 KiB
Partner
19840 
Foster
5490 
SCRP
3205 
Suffering
2549 
Rabies Risk
2539 
Other values (14)
2725 

Length

Max length19
Median length7
Mean length7.004374381
Min length3

Characters and Unicode

Total characters254595
Distinct characters36
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPartner
2nd rowPartner
3rd rowFoster
4th rowSuffering
5th rowFoster

Common Values

ValueCountFrequency (%)
Partner19840
24.9%
Foster5490
 
6.9%
SCRP3205
 
4.0%
Suffering2549
 
3.2%
Rabies Risk2539
 
3.2%
Snr752
 
0.9%
Aggressive497
 
0.6%
In Kennel351
 
0.4%
Offsite350
 
0.4%
Medical265
 
0.3%
Other values (9)510
 
0.6%
(Missing)43324
54.4%

Length

2023-03-14T02:18:28.140410image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
partner19840
50.2%
foster5667
 
14.3%
scrp3205
 
8.1%
suffering2549
 
6.5%
rabies2539
 
6.4%
risk2539
 
6.4%
snr752
 
1.9%
in545
 
1.4%
aggressive497
 
1.3%
kennel351
 
0.9%
Other values (12)1028
 
2.6%

Most occurring characters

ValueCountFrequency (%)
r49415
19.4%
e33273
13.1%
t26126
10.3%
n24514
9.6%
P23054
9.1%
a22831
9.0%
s12130
 
4.8%
i8927
 
3.5%
R8283
 
3.3%
S6523
 
2.6%
Other values (26)39519
15.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter202258
79.4%
Uppercase Letter49150
 
19.3%
Space Separator3164
 
1.2%
Other Punctuation23
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r49415
24.4%
e33273
16.5%
t26126
12.9%
n24514
12.1%
a22831
11.3%
s12130
 
6.0%
i8927
 
4.4%
o5904
 
2.9%
f5807
 
2.9%
g3611
 
1.8%
Other values (9)9720
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
P23054
46.9%
R8283
 
16.9%
S6523
 
13.3%
F5667
 
11.5%
C3228
 
6.6%
A568
 
1.2%
I568
 
1.2%
K351
 
0.7%
O350
 
0.7%
M265
 
0.5%
Other values (5)293
 
0.6%
Space Separator
ValueCountFrequency (%)
3164
100.0%
Other Punctuation
ValueCountFrequency (%)
/23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin251408
98.7%
Common3187
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
r49415
19.7%
e33273
13.2%
t26126
10.4%
n24514
9.8%
P23054
9.2%
a22831
9.1%
s12130
 
4.8%
i8927
 
3.6%
R8283
 
3.3%
S6523
 
2.6%
Other values (24)36332
14.5%
Common
ValueCountFrequency (%)
3164
99.3%
/23
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII254595
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r49415
19.4%
e33273
13.1%
t26126
10.3%
n24514
9.6%
P23054
9.1%
a22831
9.0%
s12130
 
4.8%
i8927
 
3.5%
R8283
 
3.3%
S6523
 
2.6%
Other values (26)39519
15.5%

outcome_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing10
Missing (%)< 0.1%
Memory size622.6 KiB
Adoption
33594 
Transfer
23799 
Return to Owner
14791 
Euthanasia
6244 
Died
 
690
Other values (4)
 
544

Length

Max length15
Median length8
Mean length9.423489242
Min length4

Characters and Unicode

Total characters750694
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReturn to Owner
2nd rowReturn to Owner
3rd rowReturn to Owner
4th rowTransfer
5th rowReturn to Owner

Common Values

ValueCountFrequency (%)
Adoption33594
42.2%
Transfer23799
29.9%
Return to Owner14791
18.6%
Euthanasia6244
 
7.8%
Died690
 
0.9%
Disposal304
 
0.4%
Rto-Adopt179
 
0.2%
Missing46
 
0.1%
Relocate15
 
< 0.1%
(Missing)10
 
< 0.1%

Length

2023-03-14T02:18:28.329611image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-14T02:18:28.544435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
adoption33594
30.8%
transfer23799
21.8%
return14791
13.5%
to14791
13.5%
owner14791
13.5%
euthanasia6244
 
5.7%
died690
 
0.6%
disposal304
 
0.3%
rto-adopt179
 
0.2%
missing46
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n93265
12.4%
o82656
11.0%
r77180
10.3%
t69793
 
9.3%
e54101
 
7.2%
a42850
 
5.7%
i40924
 
5.5%
d34463
 
4.6%
p34077
 
4.5%
A33773
 
4.5%
Other values (16)187612
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter626301
83.4%
Uppercase Letter94632
 
12.6%
Space Separator29582
 
3.9%
Dash Punctuation179
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n93265
14.9%
o82656
13.2%
r77180
12.3%
t69793
11.1%
e54101
8.6%
a42850
6.8%
i40924
6.5%
d34463
 
5.5%
p34077
 
5.4%
s30743
 
4.9%
Other values (7)66249
10.6%
Uppercase Letter
ValueCountFrequency (%)
A33773
35.7%
T23799
25.1%
R14985
15.8%
O14791
15.6%
E6244
 
6.6%
D994
 
1.1%
M46
 
< 0.1%
Space Separator
ValueCountFrequency (%)
29582
100.0%
Dash Punctuation
ValueCountFrequency (%)
-179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin720933
96.0%
Common29761
 
4.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n93265
12.9%
o82656
11.5%
r77180
10.7%
t69793
9.7%
e54101
 
7.5%
a42850
 
5.9%
i40924
 
5.7%
d34463
 
4.8%
p34077
 
4.7%
A33773
 
4.7%
Other values (14)157851
21.9%
Common
ValueCountFrequency (%)
29582
99.4%
-179
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII750694
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n93265
12.4%
o82656
11.0%
r77180
10.3%
t69793
 
9.3%
e54101
 
7.2%
a42850
 
5.7%
i40924
 
5.5%
d34463
 
4.6%
p34077
 
4.5%
A33773
 
4.5%
Other values (16)187612
25.0%

sex_upon_outcome
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size622.6 KiB
Neutered Male
28293 
Spayed Female
25549 
Intact Male
9732 
Intact Female
9308 
Unknown
6789 

Length

Max length13
Median length13
Mean length12.24441767
Min length7

Characters and Unicode

Total characters975525
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNeutered Male
2nd rowNeutered Male
3rd rowNeutered Male
4th rowNeutered Male
5th rowNeutered Male

Common Values

ValueCountFrequency (%)
Neutered Male28293
35.5%
Spayed Female25549
32.1%
Intact Male9732
 
12.2%
Intact Female9308
 
11.7%
Unknown6789
 
8.5%
(Missing)1
 
< 0.1%

Length

2023-03-14T02:18:28.727953image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-14T02:18:28.921260image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
male38025
24.9%
female34857
22.8%
neutered28293
18.5%
spayed25549
16.7%
intact19040
12.5%
unknown6789
 
4.5%

Most occurring characters

ValueCountFrequency (%)
e218167
22.4%
a117471
12.0%
72882
 
7.5%
l72882
 
7.5%
t66373
 
6.8%
d53842
 
5.5%
n39407
 
4.0%
M38025
 
3.9%
F34857
 
3.6%
m34857
 
3.6%
Other values (12)226762
23.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter750090
76.9%
Uppercase Letter152553
 
15.6%
Space Separator72882
 
7.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e218167
29.1%
a117471
15.7%
l72882
 
9.7%
t66373
 
8.8%
d53842
 
7.2%
n39407
 
5.3%
m34857
 
4.6%
u28293
 
3.8%
r28293
 
3.8%
p25549
 
3.4%
Other values (5)64956
 
8.7%
Uppercase Letter
ValueCountFrequency (%)
M38025
24.9%
F34857
22.8%
N28293
18.5%
S25549
16.7%
I19040
12.5%
U6789
 
4.5%
Space Separator
ValueCountFrequency (%)
72882
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin902643
92.5%
Common72882
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e218167
24.2%
a117471
13.0%
l72882
 
8.1%
t66373
 
7.4%
d53842
 
6.0%
n39407
 
4.4%
M38025
 
4.2%
F34857
 
3.9%
m34857
 
3.9%
N28293
 
3.1%
Other values (11)198469
22.0%
Common
ValueCountFrequency (%)
72882
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII975525
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e218167
22.4%
a117471
12.0%
72882
 
7.5%
l72882
 
7.5%
t66373
 
6.8%
d53842
 
5.5%
n39407
 
4.0%
M38025
 
3.9%
F34857
 
3.6%
m34857
 
3.6%
Other values (12)226762
23.2%

age_upon_outcome_(days)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct45
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean782.0461266
Minimum0
Maximum9125
Zeros94
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size622.6 KiB
2023-03-14T02:18:29.091108image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21
Q190
median365
Q31095
95-th percentile3285
Maximum9125
Range9125
Interquartile range (IQR)1005

Descriptive statistics

Standard deviation1058.528519
Coefficient of variation (CV)1.353537193
Kurtosis5.350416112
Mean782.0461266
Median Absolute Deviation (MAD)335
Skewness2.236984788
Sum62307179
Variance1120482.625
MonotonicityNot monotonic
2023-03-14T02:18:29.286188image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
36514750
18.5%
73011542
14.5%
609246
11.6%
10955277
 
6.6%
903401
 
4.3%
303396
 
4.3%
14603044
 
3.8%
18252747
 
3.4%
1202417
 
3.0%
1501955
 
2.5%
Other values (35)21897
27.5%
ValueCountFrequency (%)
094
 
0.1%
1157
 
0.2%
2226
 
0.3%
3238
 
0.3%
4136
 
0.2%
5116
 
0.1%
6152
 
0.2%
7953
1.2%
141340
1.7%
211474
1.9%
ValueCountFrequency (%)
91251
 
< 0.1%
80304
 
< 0.1%
730013
 
< 0.1%
693513
 
< 0.1%
657028
 
< 0.1%
620559
 
0.1%
5840107
 
0.1%
5475214
0.3%
5110255
0.3%
4745399
0.5%

age_upon_outcome_(years)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct45
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.142592128
Minimum0
Maximum25
Zeros94
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size622.6 KiB
2023-03-14T02:18:29.484510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05753424658
Q10.2465753425
median1
Q33
95-th percentile9
Maximum25
Range25
Interquartile range (IQR)2.753424658

Descriptive statistics

Standard deviation2.900078134
Coefficient of variation (CV)1.353537193
Kurtosis5.350416112
Mean2.142592128
Median Absolute Deviation (MAD)0.9178082192
Skewness2.236984788
Sum170704.6
Variance8.410453181
MonotonicityNot monotonic
2023-03-14T02:18:29.689271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
114750
18.5%
211542
14.5%
0.16438356169246
11.6%
35277
 
6.6%
0.24657534253401
 
4.3%
0.082191780823396
 
4.3%
43044
 
3.8%
52747
 
3.4%
0.32876712332417
 
3.0%
0.41095890411955
 
2.5%
Other values (35)21897
27.5%
ValueCountFrequency (%)
094
 
0.1%
0.002739726027157
 
0.2%
0.005479452055226
 
0.3%
0.008219178082238
 
0.3%
0.01095890411136
 
0.2%
0.01369863014116
 
0.1%
0.01643835616152
 
0.2%
0.01917808219953
1.2%
0.038356164381340
1.7%
0.057534246581474
1.9%
ValueCountFrequency (%)
251
 
< 0.1%
224
 
< 0.1%
2013
 
< 0.1%
1913
 
< 0.1%
1828
 
< 0.1%
1759
 
0.1%
16107
 
0.1%
15214
0.3%
14255
0.3%
13399
0.5%

age_upon_outcome_age_group
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size622.6 KiB
(-0.025, 2.5]
59412 
(2.5, 5.0]
11068 
(7.5, 10.0]
 
3619
(5.0, 7.5]
 
3423
(10.0, 12.5]
 
1057
Other values (5)
 
1093

Length

Max length13
Median length13
Mean length12.33651722
Min length10

Characters and Unicode

Total characters982875
Distinct characters11
Distinct categories6 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row(7.5, 10.0]
2nd row(5.0, 7.5]
3rd row(5.0, 7.5]
4th row(7.5, 10.0]
5th row(15.0, 17.5]

Common Values

ValueCountFrequency (%)
(-0.025, 2.5]59412
74.6%
(2.5, 5.0]11068
 
13.9%
(7.5, 10.0]3619
 
4.5%
(5.0, 7.5]3423
 
4.3%
(10.0, 12.5]1057
 
1.3%
(12.5, 15.0]868
 
1.1%
(15.0, 17.5]166
 
0.2%
(17.5, 20.0]54
 
0.1%
(20.0, 22.5]4
 
< 0.1%
(22.5, 25.0]1
 
< 0.1%

Length

2023-03-14T02:18:29.889475image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-14T02:18:31.224357image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
2.570480
44.2%
0.02559412
37.3%
5.014491
 
9.1%
7.57042
 
4.4%
10.04676
 
2.9%
12.51925
 
1.2%
15.01034
 
0.6%
17.5220
 
0.1%
20.058
 
< 0.1%
22.55
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
.159344
16.2%
5154610
15.7%
0143818
14.6%
2131886
13.4%
(79672
8.1%
,79672
8.1%
79672
8.1%
]79672
8.1%
-59412
 
6.0%
17855
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number445431
45.3%
Other Punctuation239016
24.3%
Open Punctuation79672
 
8.1%
Space Separator79672
 
8.1%
Close Punctuation79672
 
8.1%
Dash Punctuation59412
 
6.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5154610
34.7%
0143818
32.3%
2131886
29.6%
17855
 
1.8%
77262
 
1.6%
Other Punctuation
ValueCountFrequency (%)
.159344
66.7%
,79672
33.3%
Open Punctuation
ValueCountFrequency (%)
(79672
100.0%
Space Separator
ValueCountFrequency (%)
79672
100.0%
Close Punctuation
ValueCountFrequency (%)
]79672
100.0%
Dash Punctuation
ValueCountFrequency (%)
-59412
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common982875
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.159344
16.2%
5154610
15.7%
0143818
14.6%
2131886
13.4%
(79672
8.1%
,79672
8.1%
79672
8.1%
]79672
8.1%
-59412
 
6.0%
17855
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII982875
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.159344
16.2%
5154610
15.7%
0143818
14.6%
2131886
13.4%
(79672
8.1%
,79672
8.1%
79672
8.1%
]79672
8.1%
-59412
 
6.0%
17855
 
0.8%

outcome_datetime
Categorical

HIGH CARDINALITY
UNIFORM

Distinct65686
Distinct (%)82.4%
Missing0
Missing (%)0.0%
Memory size622.6 KiB
2016-04-18 00:00:00
 
39
2017-10-17 00:00:00
 
25
2015-08-11 00:00:00
 
25
2015-07-02 00:00:00
 
22
2015-11-17 00:00:00
 
21
Other values (65681)
79540 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1513768
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57138 ?
Unique (%)71.7%

Sample

1st row2017-12-07 14:07:00
2nd row2014-12-20 16:35:00
3rd row2014-03-08 17:10:00
4th row2014-04-07 15:12:00
5th row2013-11-16 11:54:00

Common Values

ValueCountFrequency (%)
2016-04-18 00:00:0039
 
< 0.1%
2017-10-17 00:00:0025
 
< 0.1%
2015-08-11 00:00:0025
 
< 0.1%
2015-07-02 00:00:0022
 
< 0.1%
2015-11-17 00:00:0021
 
< 0.1%
2014-10-20 09:00:0016
 
< 0.1%
2016-08-17 00:00:0016
 
< 0.1%
2016-10-29 09:00:0015
 
< 0.1%
2017-07-03 15:05:0015
 
< 0.1%
2017-08-16 00:00:0015
 
< 0.1%
Other values (65676)79463
99.7%

Length

2023-03-14T02:18:31.516220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
09:00:002095
 
1.3%
00:00:001813
 
1.1%
17:17:00248
 
0.2%
17:26:00241
 
0.2%
17:34:00236
 
0.1%
17:00:00235
 
0.1%
17:16:00233
 
0.1%
17:25:00232
 
0.1%
17:42:00229
 
0.1%
18:00:00226
 
0.1%
Other values (2684)153556
96.4%

Most occurring characters

ValueCountFrequency (%)
0375695
24.8%
1246918
16.3%
-159344
10.5%
:159344
10.5%
2154390
10.2%
79672
 
5.3%
560265
 
4.0%
459179
 
3.9%
753258
 
3.5%
349499
 
3.3%
Other values (3)116204
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1115408
73.7%
Dash Punctuation159344
 
10.5%
Other Punctuation159344
 
10.5%
Space Separator79672
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0375695
33.7%
1246918
22.1%
2154390
13.8%
560265
 
5.4%
459179
 
5.3%
753258
 
4.8%
349499
 
4.4%
648257
 
4.3%
838367
 
3.4%
929580
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
-159344
100.0%
Other Punctuation
ValueCountFrequency (%)
:159344
100.0%
Space Separator
ValueCountFrequency (%)
79672
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1513768
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0375695
24.8%
1246918
16.3%
-159344
10.5%
:159344
10.5%
2154390
10.2%
79672
 
5.3%
560265
 
4.0%
459179
 
3.9%
753258
 
3.5%
349499
 
3.3%
Other values (3)116204
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1513768
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0375695
24.8%
1246918
16.3%
-159344
10.5%
:159344
10.5%
2154390
10.2%
79672
 
5.3%
560265
 
4.0%
459179
 
3.9%
753258
 
3.5%
349499
 
3.3%
Other values (3)116204
 
7.7%

outcome_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.655424741
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size622.6 KiB
2023-03-14T02:18:32.579368image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.414283669
Coefficient of variation (CV)0.5130076294
Kurtosis-1.175114625
Mean6.655424741
Median Absolute Deviation (MAD)3
Skewness-0.06277291447
Sum530251
Variance11.65733297
MonotonicityNot monotonic
2023-03-14T02:18:32.911502image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
77424
9.3%
107219
9.1%
67009
8.8%
126845
8.6%
86840
8.6%
116837
8.6%
56830
8.6%
36743
8.5%
96421
8.1%
16250
7.8%
Other values (2)11254
14.1%
ValueCountFrequency (%)
16250
7.8%
25731
7.2%
36743
8.5%
45523
6.9%
56830
8.6%
67009
8.8%
77424
9.3%
86840
8.6%
96421
8.1%
107219
9.1%
ValueCountFrequency (%)
126845
8.6%
116837
8.6%
107219
9.1%
96421
8.1%
86840
8.6%
77424
9.3%
67009
8.8%
56830
8.6%
45523
6.9%
36743
8.5%

outcome_year
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.472563
Minimum2013
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size622.6 KiB
2023-03-14T02:18:33.170424image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2013
5-th percentile2014
Q12014
median2015
Q32017
95-th percentile2017
Maximum2018
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.305943756
Coefficient of variation (CV)0.0006479590844
Kurtosis-0.9754904079
Mean2015.472563
Median Absolute Deviation (MAD)1
Skewness0.03142165369
Sum160576730
Variance1.705489094
MonotonicityNot monotonic
2023-03-14T02:18:33.457571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
201418584
23.3%
201518505
23.2%
201617667
22.2%
201717661
22.2%
20133704
 
4.6%
20183551
 
4.5%
ValueCountFrequency (%)
20133704
 
4.6%
201418584
23.3%
201518505
23.2%
201617667
22.2%
201717661
22.2%
20183551
 
4.5%
ValueCountFrequency (%)
20183551
 
4.5%
201717661
22.2%
201617667
22.2%
201518505
23.2%
201418584
23.3%
20133704
 
4.6%

outcome_monthyear
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct55
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size622.6 KiB
2014-07
 
2072
2015-06
 
1975
2015-07
 
1900
2015-05
 
1876
2014-08
 
1851
Other values (50)
69998 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters557704
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-12
2nd row2014-12
3rd row2014-03
4th row2014-04
5th row2013-11

Common Values

ValueCountFrequency (%)
2014-072072
 
2.6%
2015-061975
 
2.5%
2015-071900
 
2.4%
2015-051876
 
2.4%
2014-081851
 
2.3%
2017-071774
 
2.2%
2014-061753
 
2.2%
2015-081735
 
2.2%
2017-081718
 
2.2%
2016-051693
 
2.1%
Other values (45)61325
77.0%

Length

2023-03-14T02:18:33.682144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2014-072072
 
2.6%
2015-061975
 
2.5%
2015-071900
 
2.4%
2015-051876
 
2.4%
2014-081851
 
2.3%
2017-071774
 
2.2%
2014-061753
 
2.2%
2015-081735
 
2.2%
2017-081718
 
2.2%
2016-051693
 
2.1%
Other values (45)61325
77.0%

Most occurring characters

ValueCountFrequency (%)
0145662
26.1%
1113660
20.4%
292248
16.5%
-79672
14.3%
525335
 
4.5%
725085
 
4.5%
624676
 
4.4%
424107
 
4.3%
310447
 
1.9%
810391
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number478032
85.7%
Dash Punctuation79672
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0145662
30.5%
1113660
23.8%
292248
19.3%
525335
 
5.3%
725085
 
5.2%
624676
 
5.2%
424107
 
5.0%
310447
 
2.2%
810391
 
2.2%
96421
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
-79672
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common557704
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0145662
26.1%
1113660
20.4%
292248
16.5%
-79672
14.3%
525335
 
4.5%
725085
 
4.5%
624676
 
4.4%
424107
 
4.3%
310447
 
1.9%
810391
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII557704
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0145662
26.1%
1113660
20.4%
292248
16.5%
-79672
14.3%
525335
 
4.5%
725085
 
4.5%
624676
 
4.4%
424107
 
4.3%
310447
 
1.9%
810391
 
1.9%

outcome_weekday
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size622.6 KiB
Saturday
12848 
Sunday
12310 
Tuesday
11688 
Monday
11429 
Friday
10735 
Other values (2)
20662 

Length

Max length9
Median length8
Mean length7.119012953
Min length6

Characters and Unicode

Total characters567186
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThursday
2nd rowSaturday
3rd rowSaturday
4th rowMonday
5th rowSaturday

Common Values

ValueCountFrequency (%)
Saturday12848
16.1%
Sunday12310
15.5%
Tuesday11688
14.7%
Monday11429
14.3%
Friday10735
13.5%
Wednesday10446
13.1%
Thursday10216
12.8%

Length

2023-03-14T02:18:33.871671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-14T02:18:34.101519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
saturday12848
16.1%
sunday12310
15.5%
tuesday11688
14.7%
monday11429
14.3%
friday10735
13.5%
wednesday10446
13.1%
thursday10216
12.8%

Most occurring characters

ValueCountFrequency (%)
a92520
16.3%
d90118
15.9%
y79672
14.0%
u47062
8.3%
n34185
 
6.0%
r33799
 
6.0%
e32580
 
5.7%
s32350
 
5.7%
S25158
 
4.4%
T21904
 
3.9%
Other values (7)77838
13.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter487514
86.0%
Uppercase Letter79672
 
14.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a92520
19.0%
d90118
18.5%
y79672
16.3%
u47062
9.7%
n34185
 
7.0%
r33799
 
6.9%
e32580
 
6.7%
s32350
 
6.6%
t12848
 
2.6%
o11429
 
2.3%
Other values (2)20951
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
S25158
31.6%
T21904
27.5%
M11429
14.3%
F10735
13.5%
W10446
13.1%

Most occurring scripts

ValueCountFrequency (%)
Latin567186
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a92520
16.3%
d90118
15.9%
y79672
14.0%
u47062
8.3%
n34185
 
6.0%
r33799
 
6.0%
e32580
 
5.7%
s32350
 
5.7%
S25158
 
4.4%
T21904
 
3.9%
Other values (7)77838
13.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII567186
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a92520
16.3%
d90118
15.9%
y79672
14.0%
u47062
8.3%
n34185
 
6.0%
r33799
 
6.0%
e32580
 
5.7%
s32350
 
5.7%
S25158
 
4.4%
T21904
 
3.9%
Other values (7)77838
13.7%

outcome_hour
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.29730646
Minimum0
Maximum23
Zeros2044
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size622.6 KiB
2023-03-14T02:18:34.267350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q112
median15
Q317
95-th percentile19
Maximum23
Range23
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.774316927
Coefficient of variation (CV)0.2639879713
Kurtosis3.220603154
Mean14.29730646
Median Absolute Deviation (MAD)2
Skewness-1.432193503
Sum1139095
Variance14.24546826
MonotonicityNot monotonic
2023-03-14T02:18:34.456193image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1712190
15.3%
1811000
13.8%
168107
10.2%
147514
9.4%
157477
9.4%
126961
8.7%
136900
8.7%
115735
7.2%
193830
 
4.8%
93713
 
4.7%
Other values (14)6245
7.8%
ValueCountFrequency (%)
02044
2.6%
114
 
< 0.1%
25
 
< 0.1%
34
 
< 0.1%
46
 
< 0.1%
517
 
< 0.1%
661
 
0.1%
7420
 
0.5%
81469
 
1.8%
93713
4.7%
ValueCountFrequency (%)
2349
 
0.1%
22188
 
0.2%
21192
 
0.2%
20204
 
0.3%
193830
 
4.8%
1811000
13.8%
1712190
15.3%
168107
10.2%
157477
9.4%
147514
9.4%

outcome_number
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.126819962
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size622.6 KiB
2023-03-14T02:18:34.643466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum13
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4560570024
Coefficient of variation (CV)0.4047292539
Kurtosis57.63880095
Mean1.126819962
Median Absolute Deviation (MAD)0
Skewness5.785008652
Sum89776
Variance0.2079879894
MonotonicityNot monotonic
2023-03-14T02:18:34.830089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
171993
90.4%
26052
 
7.6%
31152
 
1.4%
4302
 
0.4%
5101
 
0.1%
639
 
< 0.1%
715
 
< 0.1%
87
 
< 0.1%
103
 
< 0.1%
113
 
< 0.1%
Other values (3)5
 
< 0.1%
ValueCountFrequency (%)
171993
90.4%
26052
 
7.6%
31152
 
1.4%
4302
 
0.4%
5101
 
0.1%
639
 
< 0.1%
715
 
< 0.1%
87
 
< 0.1%
93
 
< 0.1%
103
 
< 0.1%
ValueCountFrequency (%)
131
 
< 0.1%
121
 
< 0.1%
113
 
< 0.1%
103
 
< 0.1%
93
 
< 0.1%
87
 
< 0.1%
715
 
< 0.1%
639
 
< 0.1%
5101
 
0.1%
4302
0.4%

dob_year
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.25487
Minimum1991
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size622.6 KiB
2023-03-14T02:18:34.999131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1991
5-th percentile2006
Q12012
median2014
Q32015
95-th percentile2017
Maximum2018
Range27
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.216516823
Coefficient of variation (CV)0.001597669958
Kurtosis3.447590339
Mean2013.25487
Median Absolute Deviation (MAD)2
Skewness-1.665329393
Sum160400042
Variance10.34598048
MonotonicityNot monotonic
2023-03-14T02:18:35.191315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
201514358
18.0%
201414334
18.0%
201311001
13.8%
201610968
13.8%
20176989
8.8%
20126209
7.8%
20113938
 
4.9%
20102547
 
3.2%
20092082
 
2.6%
20081602
 
2.0%
Other values (18)5644
 
7.1%
ValueCountFrequency (%)
19911
 
< 0.1%
19921
 
< 0.1%
19931
 
< 0.1%
19949
 
< 0.1%
19957
 
< 0.1%
199611
 
< 0.1%
199726
 
< 0.1%
199862
 
0.1%
1999109
0.1%
2000199
0.2%
ValueCountFrequency (%)
2018164
 
0.2%
20176989
8.8%
201610968
13.8%
201514358
18.0%
201414334
18.0%
201311001
13.8%
20126209
7.8%
20113938
 
4.9%
20102547
 
3.2%
20092082
 
2.6%

dob_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.31030977
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size622.6 KiB
2023-03-14T02:18:35.360695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.28907727
Coefficient of variation (CV)0.521222791
Kurtosis-1.116180732
Mean6.31030977
Median Absolute Deviation (MAD)3
Skewness0.1294755104
Sum502755
Variance10.81802929
MonotonicityNot monotonic
2023-03-14T02:18:35.505324image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
49138
11.5%
58138
10.2%
37850
9.9%
66760
8.5%
86569
8.2%
76459
8.1%
106426
8.1%
96358
8.0%
15587
7.0%
125578
7.0%
Other values (2)10809
13.6%
ValueCountFrequency (%)
15587
7.0%
25474
6.9%
37850
9.9%
49138
11.5%
58138
10.2%
66760
8.5%
76459
8.1%
86569
8.2%
96358
8.0%
106426
8.1%
ValueCountFrequency (%)
125578
7.0%
115335
6.7%
106426
8.1%
96358
8.0%
86569
8.2%
76459
8.1%
66760
8.5%
58138
10.2%
49138
11.5%
37850
9.9%

dob_monthyear
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct55
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size622.6 KiB
2014-07
 
2072
2015-06
 
1975
2015-07
 
1900
2015-05
 
1876
2014-08
 
1851
Other values (50)
69998 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters557704
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-12
2nd row2014-12
3rd row2014-03
4th row2014-04
5th row2013-11

Common Values

ValueCountFrequency (%)
2014-072072
 
2.6%
2015-061975
 
2.5%
2015-071900
 
2.4%
2015-051876
 
2.4%
2014-081851
 
2.3%
2017-071774
 
2.2%
2014-061753
 
2.2%
2015-081735
 
2.2%
2017-081718
 
2.2%
2016-051693
 
2.1%
Other values (45)61325
77.0%

Length

2023-03-14T02:18:35.684503image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2014-072072
 
2.6%
2015-061975
 
2.5%
2015-071900
 
2.4%
2015-051876
 
2.4%
2014-081851
 
2.3%
2017-071774
 
2.2%
2014-061753
 
2.2%
2015-081735
 
2.2%
2017-081718
 
2.2%
2016-051693
 
2.1%
Other values (45)61325
77.0%

Most occurring characters

ValueCountFrequency (%)
0145662
26.1%
1113660
20.4%
292248
16.5%
-79672
14.3%
525335
 
4.5%
725085
 
4.5%
624676
 
4.4%
424107
 
4.3%
310447
 
1.9%
810391
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number478032
85.7%
Dash Punctuation79672
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0145662
30.5%
1113660
23.8%
292248
19.3%
525335
 
5.3%
725085
 
5.2%
624676
 
5.2%
424107
 
5.0%
310447
 
2.2%
810391
 
2.2%
96421
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
-79672
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common557704
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0145662
26.1%
1113660
20.4%
292248
16.5%
-79672
14.3%
525335
 
4.5%
725085
 
4.5%
624676
 
4.4%
424107
 
4.3%
310447
 
1.9%
810391
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII557704
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0145662
26.1%
1113660
20.4%
292248
16.5%
-79672
14.3%
525335
 
4.5%
725085
 
4.5%
624676
 
4.4%
424107
 
4.3%
310447
 
1.9%
810391
 
1.9%

age_upon_intake
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct46
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size622.6 KiB
1 year
14580 
2 years
11529 
1 month
7458 
3 years
5206 
2 months
4023 
Other values (41)
36876 

Length

Max length9
Median length7
Mean length7.043490812
Min length5

Characters and Unicode

Total characters561169
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row10 years
2nd row7 years
3rd row6 years
4th row10 years
5th row16 years

Common Values

ValueCountFrequency (%)
1 year14580
18.3%
2 years11529
14.5%
1 month7458
 
9.4%
3 years5206
 
6.5%
2 months4023
 
5.0%
4 years3071
 
3.9%
4 weeks2819
 
3.5%
5 years2736
 
3.4%
3 weeks2276
 
2.9%
4 months2047
 
2.6%
Other values (36)23927
30.0%

Length

2023-03-14T02:18:35.859595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
years32058
20.1%
123577
14.8%
217419
10.9%
months16026
10.1%
year14580
9.2%
39765
 
6.1%
48123
 
5.1%
month7458
 
4.7%
weeks7418
 
4.7%
55067
 
3.2%
Other values (21)17853
11.2%

Most occurring characters

ValueCountFrequency (%)
79672
14.2%
e62810
11.2%
s56621
10.1%
y48102
8.6%
a48102
8.6%
r46638
 
8.3%
129028
 
5.2%
n23484
 
4.2%
t23484
 
4.2%
h23484
 
4.2%
Other values (14)119744
21.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter397329
70.8%
Decimal Number84168
 
15.0%
Space Separator79672
 
14.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e62810
15.8%
s56621
14.3%
y48102
12.1%
a48102
12.1%
r46638
11.7%
n23484
 
5.9%
t23484
 
5.9%
h23484
 
5.9%
o23484
 
5.9%
m23484
 
5.9%
Other values (3)17636
 
4.4%
Decimal Number
ValueCountFrequency (%)
129028
34.5%
218032
21.4%
310144
 
12.1%
48372
 
9.9%
55282
 
6.3%
63682
 
4.4%
72784
 
3.3%
82445
 
2.9%
02330
 
2.8%
92069
 
2.5%
Space Separator
ValueCountFrequency (%)
79672
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin397329
70.8%
Common163840
29.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e62810
15.8%
s56621
14.3%
y48102
12.1%
a48102
12.1%
r46638
11.7%
n23484
 
5.9%
t23484
 
5.9%
h23484
 
5.9%
o23484
 
5.9%
m23484
 
5.9%
Other values (3)17636
 
4.4%
Common
ValueCountFrequency (%)
79672
48.6%
129028
 
17.7%
218032
 
11.0%
310144
 
6.2%
48372
 
5.1%
55282
 
3.2%
63682
 
2.2%
72784
 
1.7%
82445
 
1.5%
02330
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII561169
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
79672
14.2%
e62810
11.2%
s56621
10.1%
y48102
8.6%
a48102
8.6%
r46638
 
8.3%
129028
 
5.2%
n23484
 
4.2%
t23484
 
4.2%
h23484
 
4.2%
Other values (14)119744
21.3%

animal_id_intake
Categorical

HIGH CARDINALITY
UNIFORM

Distinct71961
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Memory size622.6 KiB
A721033
 
13
A718223
 
11
A706536
 
11
A694501
 
8
A716018
 
8
Other values (71956)
79621 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters557704
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65890 ?
Unique (%)82.7%

Sample

1st rowA006100
2nd rowA006100
3rd rowA006100
4th rowA047759
5th rowA134067

Common Values

ValueCountFrequency (%)
A72103313
 
< 0.1%
A71822311
 
< 0.1%
A70653611
 
< 0.1%
A6945018
 
< 0.1%
A7160188
 
< 0.1%
A7383248
 
< 0.1%
A6164448
 
< 0.1%
A7356017
 
< 0.1%
A6782947
 
< 0.1%
A5935377
 
< 0.1%
Other values (71951)79584
99.9%

Length

2023-03-14T02:18:36.041381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a72103313
 
< 0.1%
a70653611
 
< 0.1%
a71822311
 
< 0.1%
a6945018
 
< 0.1%
a7160188
 
< 0.1%
a7383248
 
< 0.1%
a6164448
 
< 0.1%
a7380737
 
< 0.1%
a6831087
 
< 0.1%
a7019017
 
< 0.1%
Other values (71951)79584
99.9%

Most occurring characters

ValueCountFrequency (%)
788898
15.9%
A79672
14.3%
670578
12.7%
541173
7.4%
340062
7.2%
839800
7.1%
039734
7.1%
139721
7.1%
439721
7.1%
239392
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number478032
85.7%
Uppercase Letter79672
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
788898
18.6%
670578
14.8%
541173
8.6%
340062
8.4%
839800
8.3%
039734
8.3%
139721
8.3%
439721
8.3%
239392
8.2%
938953
8.1%
Uppercase Letter
ValueCountFrequency (%)
A79672
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common478032
85.7%
Latin79672
 
14.3%

Most frequent character per script

Common
ValueCountFrequency (%)
788898
18.6%
670578
14.8%
541173
8.6%
340062
8.4%
839800
8.3%
039734
8.3%
139721
8.3%
439721
8.3%
239392
8.2%
938953
8.1%
Latin
ValueCountFrequency (%)
A79672
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII557704
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
788898
15.9%
A79672
14.3%
670578
12.7%
541173
7.4%
340062
7.2%
839800
7.1%
039734
7.1%
139721
7.1%
439721
7.1%
239392
7.1%

animal_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size622.6 KiB
Dog
45366 
Cat
29539 
Other
 
4428
Bird
 
339

Length

Max length5
Median length3
Mean length3.115410684
Min length3

Characters and Unicode

Total characters248211
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDog
2nd rowDog
3rd rowDog
4th rowDog
5th rowDog

Common Values

ValueCountFrequency (%)
Dog45366
56.9%
Cat29539
37.1%
Other4428
 
5.6%
Bird339
 
0.4%

Length

2023-03-14T02:18:36.220448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-14T02:18:36.411437image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
dog45366
56.9%
cat29539
37.1%
other4428
 
5.6%
bird339
 
0.4%

Most occurring characters

ValueCountFrequency (%)
D45366
18.3%
o45366
18.3%
g45366
18.3%
t33967
13.7%
C29539
11.9%
a29539
11.9%
r4767
 
1.9%
O4428
 
1.8%
h4428
 
1.8%
e4428
 
1.8%
Other values (3)1017
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter168539
67.9%
Uppercase Letter79672
32.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o45366
26.9%
g45366
26.9%
t33967
20.2%
a29539
17.5%
r4767
 
2.8%
h4428
 
2.6%
e4428
 
2.6%
i339
 
0.2%
d339
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
D45366
56.9%
C29539
37.1%
O4428
 
5.6%
B339
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin248211
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D45366
18.3%
o45366
18.3%
g45366
18.3%
t33967
13.7%
C29539
11.9%
a29539
11.9%
r4767
 
1.9%
O4428
 
1.8%
h4428
 
1.8%
e4428
 
1.8%
Other values (3)1017
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII248211
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D45366
18.3%
o45366
18.3%
g45366
18.3%
t33967
13.7%
C29539
11.9%
a29539
11.9%
r4767
 
1.9%
O4428
 
1.8%
h4428
 
1.8%
e4428
 
1.8%
Other values (3)1017
 
0.4%

breed
Categorical

HIGH CARDINALITY

Distinct2155
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size622.6 KiB
Domestic Shorthair Mix
23423 
Pit Bull Mix
6256 
Chihuahua Shorthair Mix
4831 
Labrador Retriever Mix
4789 
Domestic Medium Hair Mix
 
2326
Other values (2150)
38047 

Length

Max length54
Median length51
Mean length19.58070589
Min length3

Characters and Unicode

Total characters1560034
Distinct characters55
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique879 ?
Unique (%)1.1%

Sample

1st rowSpinone Italiano Mix
2nd rowSpinone Italiano Mix
3rd rowSpinone Italiano Mix
4th rowDachshund
5th rowShetland Sheepdog

Common Values

ValueCountFrequency (%)
Domestic Shorthair Mix23423
29.4%
Pit Bull Mix6256
 
7.9%
Chihuahua Shorthair Mix4831
 
6.1%
Labrador Retriever Mix4789
 
6.0%
Domestic Medium Hair Mix2326
 
2.9%
German Shepherd Mix1950
 
2.4%
Bat Mix1381
 
1.7%
Domestic Longhair Mix1248
 
1.6%
Australian Cattle Dog Mix1099
 
1.4%
Siamese Mix996
 
1.3%
Other values (2145)31373
39.4%

Length

2023-03-14T02:18:36.593464image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mix66999
29.5%
shorthair29746
13.1%
domestic27579
12.2%
bull7292
 
3.2%
pit7075
 
3.1%
retriever6327
 
2.8%
labrador6260
 
2.8%
chihuahua6172
 
2.7%
terrier3846
 
1.7%
shepherd3547
 
1.6%
Other values (1577)61971
27.3%

Most occurring characters

ValueCountFrequency (%)
i181668
11.6%
147142
 
9.4%
r139246
 
8.9%
e107112
 
6.9%
h105116
 
6.7%
a100850
 
6.5%
t94007
 
6.0%
o90654
 
5.8%
M72890
 
4.7%
x68591
 
4.4%
Other values (45)452758
29.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1171178
75.1%
Uppercase Letter234234
 
15.0%
Space Separator147142
 
9.4%
Other Punctuation7440
 
0.5%
Dash Punctuation40
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i181668
15.5%
r139246
11.9%
e107112
9.1%
h105116
9.0%
a100850
8.6%
t94007
8.0%
o90654
7.7%
x68591
 
5.9%
s43798
 
3.7%
u39015
 
3.3%
Other values (16)201121
17.2%
Uppercase Letter
ValueCountFrequency (%)
M72890
31.1%
S40707
17.4%
D32114
13.7%
B15857
 
6.8%
C14232
 
6.1%
P12382
 
5.3%
R11524
 
4.9%
L9659
 
4.1%
T5528
 
2.4%
A4965
 
2.1%
Other values (15)14376
 
6.1%
Other Punctuation
ValueCountFrequency (%)
/7380
99.2%
.60
 
0.8%
Space Separator
ValueCountFrequency (%)
147142
100.0%
Dash Punctuation
ValueCountFrequency (%)
-40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1405412
90.1%
Common154622
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i181668
12.9%
r139246
 
9.9%
e107112
 
7.6%
h105116
 
7.5%
a100850
 
7.2%
t94007
 
6.7%
o90654
 
6.5%
M72890
 
5.2%
x68591
 
4.9%
s43798
 
3.1%
Other values (41)401480
28.6%
Common
ValueCountFrequency (%)
147142
95.2%
/7380
 
4.8%
.60
 
< 0.1%
-40
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1560034
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i181668
11.6%
147142
 
9.4%
r139246
 
8.9%
e107112
 
6.9%
h105116
 
6.7%
a100850
 
6.5%
t94007
 
6.0%
o90654
 
5.8%
M72890
 
4.7%
x68591
 
4.4%
Other values (45)452758
29.0%

color
Categorical

HIGH CARDINALITY

Distinct529
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size622.6 KiB
Black/White
8270 
Black
6673 
Brown Tabby
 
4471
Brown
 
3598
White
 
2835
Other values (524)
53825 

Length

Max length27
Median length24
Mean length9.538796566
Min length3

Characters and Unicode

Total characters759975
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique148 ?
Unique (%)0.2%

Sample

1st rowYellow/White
2nd rowYellow/White
3rd rowYellow/White
4th rowTricolor
5th rowBrown/White

Common Values

ValueCountFrequency (%)
Black/White8270
 
10.4%
Black6673
 
8.4%
Brown Tabby4471
 
5.6%
Brown3598
 
4.5%
White2835
 
3.6%
Brown/White2516
 
3.2%
Tan/White2462
 
3.1%
Brown Tabby/White2351
 
3.0%
Orange Tabby2182
 
2.7%
White/Black2172
 
2.7%
Other values (519)42142
52.9%

Length

2023-03-14T02:18:37.340564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
brown12847
 
12.9%
tabby9193
 
9.3%
black/white8270
 
8.3%
black7351
 
7.4%
tabby/white4393
 
4.4%
blue3702
 
3.7%
orange3370
 
3.4%
white2835
 
2.9%
brown/white2516
 
2.5%
tan/white2462
 
2.5%
Other values (334)42361
42.7%

Most occurring characters

ValueCountFrequency (%)
e61832
 
8.1%
a61137
 
8.0%
B57048
 
7.5%
i47523
 
6.3%
l44680
 
5.9%
/41904
 
5.5%
r41383
 
5.4%
n40277
 
5.3%
t39931
 
5.3%
h36815
 
4.8%
Other values (28)287445
37.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter557239
73.3%
Uppercase Letter141204
 
18.6%
Other Punctuation41904
 
5.5%
Space Separator19628
 
2.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e61832
11.1%
a61137
11.0%
i47523
8.5%
l44680
 
8.0%
r41383
 
7.4%
n40277
 
7.2%
t39931
 
7.2%
h36815
 
6.6%
o36282
 
6.5%
c31209
 
5.6%
Other values (12)116170
20.8%
Uppercase Letter
ValueCountFrequency (%)
B57048
40.4%
W35399
25.1%
T28582
20.2%
C4742
 
3.4%
O3707
 
2.6%
R2860
 
2.0%
G2553
 
1.8%
S1645
 
1.2%
P1509
 
1.1%
Y804
 
0.6%
Other values (4)2355
 
1.7%
Other Punctuation
ValueCountFrequency (%)
/41904
100.0%
Space Separator
ValueCountFrequency (%)
19628
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin698443
91.9%
Common61532
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e61832
 
8.9%
a61137
 
8.8%
B57048
 
8.2%
i47523
 
6.8%
l44680
 
6.4%
r41383
 
5.9%
n40277
 
5.8%
t39931
 
5.7%
h36815
 
5.3%
o36282
 
5.2%
Other values (26)231535
33.2%
Common
ValueCountFrequency (%)
/41904
68.1%
19628
31.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII759975
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e61832
 
8.1%
a61137
 
8.0%
B57048
 
7.5%
i47523
 
6.3%
l44680
 
5.9%
/41904
 
5.5%
r41383
 
5.4%
n40277
 
5.3%
t39931
 
5.3%
h36815
 
4.8%
Other values (28)287445
37.8%

found_location
Categorical

HIGH CARDINALITY

Distinct36576
Distinct (%)45.9%
Missing0
Missing (%)0.0%
Memory size622.6 KiB
Austin (TX)
14311 
Outside Jurisdiction
 
945
Travis (TX)
 
907
7201 Levander Loop in Austin (TX)
 
514
Del Valle (TX)
 
407
Other values (36571)
62588 

Length

Max length85
Median length69
Mean length28.85169194
Min length9

Characters and Unicode

Total characters2298672
Distinct characters83
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27167 ?
Unique (%)34.1%

Sample

1st rowColony Creek And Hunters Trace in Austin (TX)
2nd row8700 Research Blvd in Austin (TX)
3rd row8700 Research in Austin (TX)
4th rowAustin (TX)
5th row12034 Research Blvd in Austin (TX)

Common Values

ValueCountFrequency (%)
Austin (TX)14311
 
18.0%
Outside Jurisdiction945
 
1.2%
Travis (TX)907
 
1.1%
7201 Levander Loop in Austin (TX)514
 
0.6%
Del Valle (TX)407
 
0.5%
Pflugerville (TX)370
 
0.5%
Manor (TX)268
 
0.3%
4434 Frontier Trl in Austin (TX)162
 
0.2%
124 W Anderson Ln in Austin (TX)152
 
0.2%
Leander (TX)112
 
0.1%
Other values (36566)61524
77.2%

Length

2023-03-14T02:18:37.564027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tx78811
17.7%
austin66966
 
15.0%
in62022
 
13.9%
dr10271
 
2.3%
and9425
 
2.1%
rd6754
 
1.5%
travis6293
 
1.4%
ln5542
 
1.2%
st5407
 
1.2%
e3586
 
0.8%
Other values (13635)190944
42.8%

Most occurring characters

ValueCountFrequency (%)
366837
 
16.0%
n190104
 
8.3%
i176256
 
7.7%
t103953
 
4.5%
s103931
 
4.5%
T92192
 
4.0%
u82957
 
3.6%
A82605
 
3.6%
e79886
 
3.5%
X78759
 
3.4%
Other values (73)941192
40.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1148452
50.0%
Uppercase Letter408748
 
17.8%
Space Separator366837
 
16.0%
Decimal Number209877
 
9.1%
Open Punctuation78755
 
3.4%
Close Punctuation78752
 
3.4%
Other Punctuation6830
 
0.3%
Dash Punctuation411
 
< 0.1%
Math Symbol6
 
< 0.1%
Modifier Symbol4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n190104
16.6%
i176256
15.3%
t103953
9.1%
s103931
9.0%
u82957
7.2%
e79886
7.0%
r75791
 
6.6%
a69452
 
6.0%
l49601
 
4.3%
o47298
 
4.1%
Other values (16)169223
14.7%
Uppercase Letter
ValueCountFrequency (%)
T92192
22.6%
A82605
20.2%
X78759
19.3%
S17857
 
4.4%
D17387
 
4.3%
L14440
 
3.5%
R14299
 
3.5%
C14287
 
3.5%
B12502
 
3.1%
W8967
 
2.2%
Other values (16)55453
13.6%
Other Punctuation
ValueCountFrequency (%)
&2997
43.9%
.1542
22.6%
/1148
 
16.8%
#903
 
13.2%
,141
 
2.1%
'58
 
0.8%
@33
 
0.5%
?4
 
0.1%
\2
 
< 0.1%
*1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
045635
21.7%
145343
21.6%
223082
11.0%
317026
 
8.1%
516639
 
7.9%
414594
 
7.0%
612417
 
5.9%
712397
 
5.9%
811834
 
5.6%
910910
 
5.2%
Open Punctuation
ValueCountFrequency (%)
(78748
> 99.9%
{4
 
< 0.1%
[3
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
)78746
> 99.9%
}5
 
< 0.1%
]1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
366837
100.0%
Dash Punctuation
ValueCountFrequency (%)
-411
100.0%
Math Symbol
ValueCountFrequency (%)
+6
100.0%
Modifier Symbol
ValueCountFrequency (%)
`4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1557200
67.7%
Common741472
32.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
n190104
 
12.2%
i176256
 
11.3%
t103953
 
6.7%
s103931
 
6.7%
T92192
 
5.9%
u82957
 
5.3%
A82605
 
5.3%
e79886
 
5.1%
X78759
 
5.1%
r75791
 
4.9%
Other values (42)490766
31.5%
Common
ValueCountFrequency (%)
366837
49.5%
(78748
 
10.6%
)78746
 
10.6%
045635
 
6.2%
145343
 
6.1%
223082
 
3.1%
317026
 
2.3%
516639
 
2.2%
414594
 
2.0%
612417
 
1.7%
Other values (21)42405
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2298672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
366837
 
16.0%
n190104
 
8.3%
i176256
 
7.7%
t103953
 
4.5%
s103931
 
4.5%
T92192
 
4.0%
u82957
 
3.6%
A82605
 
3.6%
e79886
 
3.5%
X78759
 
3.4%
Other values (73)941192
40.9%

intake_condition
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size622.6 KiB
Normal
70056 
Injured
 
3997
Sick
 
3099
Nursing
 
1915
Aged
 
319
Other values (3)
 
286

Length

Max length8
Median length6
Mean length5.986582488
Min length4

Characters and Unicode

Total characters476963
Distinct characters24
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowNormal
3rd rowNormal
4th rowNormal
5th rowInjured

Common Values

ValueCountFrequency (%)
Normal70056
87.9%
Injured3997
 
5.0%
Sick3099
 
3.9%
Nursing1915
 
2.4%
Aged319
 
0.4%
Other147
 
0.2%
Feral92
 
0.1%
Pregnant47
 
0.1%

Length

2023-03-14T02:18:37.790653image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-14T02:18:38.005471image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
normal70056
87.9%
injured3997
 
5.0%
sick3099
 
3.9%
nursing1915
 
2.4%
aged319
 
0.4%
other147
 
0.2%
feral92
 
0.1%
pregnant47
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r76254
16.0%
N71971
15.1%
a70195
14.7%
l70148
14.7%
m70056
14.7%
o70056
14.7%
n6006
 
1.3%
u5912
 
1.2%
i5014
 
1.1%
e4602
 
1.0%
Other values (14)26749
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter397291
83.3%
Uppercase Letter79672
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r76254
19.2%
a70195
17.7%
l70148
17.7%
m70056
17.6%
o70056
17.6%
n6006
 
1.5%
u5912
 
1.5%
i5014
 
1.3%
e4602
 
1.2%
d4316
 
1.1%
Other values (7)14732
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
N71971
90.3%
I3997
 
5.0%
S3099
 
3.9%
A319
 
0.4%
O147
 
0.2%
F92
 
0.1%
P47
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin476963
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r76254
16.0%
N71971
15.1%
a70195
14.7%
l70148
14.7%
m70056
14.7%
o70056
14.7%
n6006
 
1.3%
u5912
 
1.2%
i5014
 
1.1%
e4602
 
1.0%
Other values (14)26749
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII476963
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r76254
16.0%
N71971
15.1%
a70195
14.7%
l70148
14.7%
m70056
14.7%
o70056
14.7%
n6006
 
1.3%
u5912
 
1.2%
i5014
 
1.1%
e4602
 
1.0%
Other values (14)26749
 
5.6%

intake_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size622.6 KiB
Stray
55935 
Owner Surrender
15028 
Public Assist
 
4994
Wildlife
 
3464
Euthanasia Request
 
251

Length

Max length18
Median length5
Mean length7.559079727
Min length5

Characters and Unicode

Total characters602247
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStray
2nd rowPublic Assist
3rd rowPublic Assist
4th rowOwner Surrender
5th rowPublic Assist

Common Values

ValueCountFrequency (%)
Stray55935
70.2%
Owner Surrender15028
 
18.9%
Public Assist4994
 
6.3%
Wildlife3464
 
4.3%
Euthanasia Request251
 
0.3%

Length

2023-03-14T02:18:38.177005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-14T02:18:38.366229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
stray55935
56.0%
owner15028
 
15.0%
surrender15028
 
15.0%
public4994
 
5.0%
assist4994
 
5.0%
wildlife3464
 
3.5%
euthanasia251
 
0.3%
request251
 
0.3%

Most occurring characters

ValueCountFrequency (%)
r116047
19.3%
S70963
11.8%
t61431
10.2%
a56688
9.4%
y55935
9.3%
e49050
8.1%
n30307
 
5.0%
u20524
 
3.4%
20273
 
3.4%
d18492
 
3.1%
Other values (15)102537
17.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter482029
80.0%
Uppercase Letter99945
 
16.6%
Space Separator20273
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r116047
24.1%
t61431
12.7%
a56688
11.8%
y55935
11.6%
e49050
10.2%
n30307
 
6.3%
u20524
 
4.3%
d18492
 
3.8%
i17167
 
3.6%
s15484
 
3.2%
Other values (7)40904
 
8.5%
Uppercase Letter
ValueCountFrequency (%)
S70963
71.0%
O15028
 
15.0%
A4994
 
5.0%
P4994
 
5.0%
W3464
 
3.5%
E251
 
0.3%
R251
 
0.3%
Space Separator
ValueCountFrequency (%)
20273
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin581974
96.6%
Common20273
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
r116047
19.9%
S70963
12.2%
t61431
10.6%
a56688
9.7%
y55935
9.6%
e49050
8.4%
n30307
 
5.2%
u20524
 
3.5%
d18492
 
3.2%
i17167
 
2.9%
Other values (14)85370
14.7%
Common
ValueCountFrequency (%)
20273
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII602247
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r116047
19.3%
S70963
11.8%
t61431
10.2%
a56688
9.4%
y55935
9.3%
e49050
8.1%
n30307
 
5.0%
u20524
 
3.4%
20273
 
3.4%
d18492
 
3.1%
Other values (15)102537
17.0%

sex_upon_intake
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size622.6 KiB
Intact Male
25317 
Intact Female
23704 
Neutered Male
12708 
Spayed Female
11153 
Unknown
6789 

Length

Max length13
Median length13
Mean length11.85318372
Min length7

Characters and Unicode

Total characters944355
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNeutered Male
2nd rowNeutered Male
3rd rowNeutered Male
4th rowNeutered Male
5th rowNeutered Male

Common Values

ValueCountFrequency (%)
Intact Male25317
31.8%
Intact Female23704
29.8%
Neutered Male12708
16.0%
Spayed Female11153
14.0%
Unknown6789
 
8.5%
(Missing)1
 
< 0.1%

Length

2023-03-14T02:18:38.523754image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-14T02:18:38.719903image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
intact49021
32.1%
male38025
24.9%
female34857
22.8%
neutered12708
 
8.3%
spayed11153
 
7.3%
unknown6789
 
4.5%

Most occurring characters

ValueCountFrequency (%)
e157016
16.6%
a133056
14.1%
t110750
11.7%
72882
7.7%
l72882
7.7%
n69388
7.3%
I49021
 
5.2%
c49021
 
5.2%
M38025
 
4.0%
F34857
 
3.7%
Other values (12)157457
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter718920
76.1%
Uppercase Letter152553
 
16.2%
Space Separator72882
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e157016
21.8%
a133056
18.5%
t110750
15.4%
l72882
10.1%
n69388
9.7%
c49021
 
6.8%
m34857
 
4.8%
d23861
 
3.3%
r12708
 
1.8%
u12708
 
1.8%
Other values (5)42673
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
I49021
32.1%
M38025
24.9%
F34857
22.8%
N12708
 
8.3%
S11153
 
7.3%
U6789
 
4.5%
Space Separator
ValueCountFrequency (%)
72882
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin871473
92.3%
Common72882
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e157016
18.0%
a133056
15.3%
t110750
12.7%
l72882
8.4%
n69388
8.0%
I49021
 
5.6%
c49021
 
5.6%
M38025
 
4.4%
F34857
 
4.0%
m34857
 
4.0%
Other values (11)122600
14.1%
Common
ValueCountFrequency (%)
72882
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII944355
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e157016
16.6%
a133056
14.1%
t110750
11.7%
72882
7.7%
l72882
7.7%
n69388
7.3%
I49021
 
5.2%
c49021
 
5.2%
M38025
 
4.0%
F34857
 
3.7%
Other values (12)157457
16.7%

count
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size622.6 KiB
1
79672 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters79672
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
179672
100.0%

Length

2023-03-14T02:18:38.906103image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-14T02:18:39.074643image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
179672
100.0%

Most occurring characters

ValueCountFrequency (%)
179672
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number79672
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
179672
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common79672
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
179672
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII79672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
179672
100.0%

age_upon_intake_(days)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct45
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean769.341701
Minimum0
Maximum9125
Zeros450
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size622.6 KiB
2023-03-14T02:18:39.218213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q160
median365
Q31095
95-th percentile3285
Maximum9125
Range9125
Interquartile range (IQR)1035

Descriptive statistics

Standard deviation1056.00904
Coefficient of variation (CV)1.372613806
Kurtosis5.393341764
Mean769.341701
Median Absolute Deviation (MAD)337
Skewness2.237217287
Sum61294992
Variance1115155.093
MonotonicityNot monotonic
2023-03-14T02:18:39.414778image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
36514580
18.3%
73011529
14.5%
307458
 
9.4%
10955206
 
6.5%
604023
 
5.0%
14603071
 
3.9%
282819
 
3.5%
18252736
 
3.4%
212276
 
2.9%
1202047
 
2.6%
Other values (35)23927
30.0%
ValueCountFrequency (%)
0450
 
0.6%
1345
 
0.4%
2271
 
0.3%
3347
 
0.4%
4186
 
0.2%
5125
 
0.2%
6190
 
0.2%
71194
1.5%
141596
2.0%
212276
2.9%
ValueCountFrequency (%)
91251
 
< 0.1%
80304
 
< 0.1%
730013
 
< 0.1%
693513
 
< 0.1%
657027
 
< 0.1%
620559
 
0.1%
5840106
 
0.1%
5475214
0.3%
5110249
0.3%
4745379
0.5%

age_upon_intake_(years)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct45
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.107785482
Minimum0
Maximum25
Zeros450
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size622.6 KiB
2023-03-14T02:18:39.605886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03835616438
Q10.1643835616
median1
Q33
95-th percentile9
Maximum25
Range25
Interquartile range (IQR)2.835616438

Descriptive statistics

Standard deviation2.893175453
Coefficient of variation (CV)1.372613806
Kurtosis5.393341764
Mean2.107785482
Median Absolute Deviation (MAD)0.9232876712
Skewness2.237217287
Sum167931.4849
Variance8.370464203
MonotonicityNot monotonic
2023-03-14T02:18:39.829683image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
114580
18.3%
211529
14.5%
0.082191780827458
 
9.4%
35206
 
6.5%
0.16438356164023
 
5.0%
43071
 
3.9%
0.076712328772819
 
3.5%
52736
 
3.4%
0.057534246582276
 
2.9%
0.32876712332047
 
2.6%
Other values (35)23927
30.0%
ValueCountFrequency (%)
0450
 
0.6%
0.002739726027345
 
0.4%
0.005479452055271
 
0.3%
0.008219178082347
 
0.4%
0.01095890411186
 
0.2%
0.01369863014125
 
0.2%
0.01643835616190
 
0.2%
0.019178082191194
1.5%
0.038356164381596
2.0%
0.057534246582276
2.9%
ValueCountFrequency (%)
251
 
< 0.1%
224
 
< 0.1%
2013
 
< 0.1%
1913
 
< 0.1%
1827
 
< 0.1%
1759
 
0.1%
16106
 
0.1%
15214
0.3%
14249
0.3%
13379
0.5%

age_upon_intake_age_group
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size622.6 KiB
(-0.025, 2.5]
59593 
(2.5, 5.0]
11013 
(7.5, 10.0]
 
3539
(5.0, 7.5]
 
3422
(10.0, 12.5]
 
1040
Other values (5)
 
1065

Length

Max length13
Median length13
Mean length12.34119892
Min length10

Characters and Unicode

Total characters983248
Distinct characters11
Distinct categories6 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row(7.5, 10.0]
2nd row(5.0, 7.5]
3rd row(5.0, 7.5]
4th row(7.5, 10.0]
5th row(15.0, 17.5]

Common Values

ValueCountFrequency (%)
(-0.025, 2.5]59593
74.8%
(2.5, 5.0]11013
 
13.8%
(7.5, 10.0]3539
 
4.4%
(5.0, 7.5]3422
 
4.3%
(10.0, 12.5]1040
 
1.3%
(12.5, 15.0]842
 
1.1%
(15.0, 17.5]165
 
0.2%
(17.5, 20.0]53
 
0.1%
(20.0, 22.5]4
 
< 0.1%
(22.5, 25.0]1
 
< 0.1%

Length

2023-03-14T02:18:40.029220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-14T02:18:40.241925image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
2.570606
44.3%
0.02559593
37.4%
5.014435
 
9.1%
7.56961
 
4.4%
10.04579
 
2.9%
12.51882
 
1.2%
15.01007
 
0.6%
17.5218
 
0.1%
20.057
 
< 0.1%
22.55
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
.159344
16.2%
5154708
15.7%
0143901
14.6%
2132149
13.4%
(79672
8.1%
,79672
8.1%
79672
8.1%
]79672
8.1%
-59593
 
6.1%
17686
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number445623
45.3%
Other Punctuation239016
24.3%
Open Punctuation79672
 
8.1%
Space Separator79672
 
8.1%
Close Punctuation79672
 
8.1%
Dash Punctuation59593
 
6.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5154708
34.7%
0143901
32.3%
2132149
29.7%
17686
 
1.7%
77179
 
1.6%
Other Punctuation
ValueCountFrequency (%)
.159344
66.7%
,79672
33.3%
Open Punctuation
ValueCountFrequency (%)
(79672
100.0%
Space Separator
ValueCountFrequency (%)
79672
100.0%
Close Punctuation
ValueCountFrequency (%)
]79672
100.0%
Dash Punctuation
ValueCountFrequency (%)
-59593
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common983248
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.159344
16.2%
5154708
15.7%
0143901
14.6%
2132149
13.4%
(79672
8.1%
,79672
8.1%
79672
8.1%
]79672
8.1%
-59593
 
6.1%
17686
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII983248
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.159344
16.2%
5154708
15.7%
0143901
14.6%
2132149
13.4%
(79672
8.1%
,79672
8.1%
79672
8.1%
]79672
8.1%
-59593
 
6.1%
17686
 
0.8%

intake_datetime
Categorical

HIGH CARDINALITY

Distinct56747
Distinct (%)71.2%
Missing0
Missing (%)0.0%
Memory size622.6 KiB
2016-09-23 12:00:00
 
64
2014-07-09 12:58:00
 
63
2017-09-01 14:47:00
 
59
2014-02-19 13:51:00
 
49
2016-02-14 16:02:00
 
45
Other values (56742)
79392 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1513768
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45328 ?
Unique (%)56.9%

Sample

1st row2017-12-07 00:00:00
2nd row2014-12-19 10:21:00
3rd row2014-03-07 14:26:00
4th row2014-04-02 15:55:00
5th row2013-11-16 09:02:00

Common Values

ValueCountFrequency (%)
2016-09-23 12:00:0064
 
0.1%
2014-07-09 12:58:0063
 
0.1%
2017-09-01 14:47:0059
 
0.1%
2014-02-19 13:51:0049
 
0.1%
2016-02-14 16:02:0045
 
0.1%
2014-08-15 18:48:0038
 
< 0.1%
2017-05-06 15:11:0032
 
< 0.1%
2013-12-04 23:12:0023
 
< 0.1%
2016-09-03 12:04:0022
 
< 0.1%
2016-08-26 13:09:0020
 
< 0.1%
Other values (56737)79257
99.5%

Length

2023-03-14T02:18:40.414285image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11:01:00482
 
0.3%
11:02:00404
 
0.3%
11:00:00334
 
0.2%
11:03:00323
 
0.2%
11:04:00290
 
0.2%
11:05:00288
 
0.2%
12:00:00272
 
0.2%
11:07:00258
 
0.2%
11:29:00252
 
0.2%
12:58:00247
 
0.2%
Other values (2900)156194
98.0%

Most occurring characters

ValueCountFrequency (%)
0370364
24.5%
1255603
16.9%
2159806
10.6%
-159344
10.5%
:159344
10.5%
79672
 
5.3%
563262
 
4.2%
461919
 
4.1%
353402
 
3.5%
648548
 
3.2%
Other values (3)102504
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1115408
73.7%
Dash Punctuation159344
 
10.5%
Other Punctuation159344
 
10.5%
Space Separator79672
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0370364
33.2%
1255603
22.9%
2159806
14.3%
563262
 
5.7%
461919
 
5.6%
353402
 
4.8%
648548
 
4.4%
746079
 
4.1%
831561
 
2.8%
924864
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
-159344
100.0%
Other Punctuation
ValueCountFrequency (%)
:159344
100.0%
Space Separator
ValueCountFrequency (%)
79672
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1513768
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0370364
24.5%
1255603
16.9%
2159806
10.6%
-159344
10.5%
:159344
10.5%
79672
 
5.3%
563262
 
4.2%
461919
 
4.1%
353402
 
3.5%
648548
 
3.2%
Other values (3)102504
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1513768
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0370364
24.5%
1255603
16.9%
2159806
10.6%
-159344
10.5%
:159344
10.5%
79672
 
5.3%
563262
 
4.2%
461919
 
4.1%
353402
 
3.5%
648548
 
3.2%
Other values (3)102504
 
6.8%

intake_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.584032031
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size622.6 KiB
2023-03-14T02:18:40.571663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.366578833
Coefficient of variation (CV)0.5113247957
Kurtosis-1.164640683
Mean6.584032031
Median Absolute Deviation (MAD)3
Skewness-0.02599353788
Sum524563
Variance11.33385304
MonotonicityNot monotonic
2023-03-14T02:18:40.737225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
57966
10.0%
107652
9.6%
67459
9.4%
36613
8.3%
116597
8.3%
76528
8.2%
86498
8.2%
96489
8.1%
46112
7.7%
126099
7.7%
Other values (2)11659
14.6%
ValueCountFrequency (%)
15982
7.5%
25677
7.1%
36613
8.3%
46112
7.7%
57966
10.0%
67459
9.4%
76528
8.2%
86498
8.2%
96489
8.1%
107652
9.6%
ValueCountFrequency (%)
126099
7.7%
116597
8.3%
107652
9.6%
96489
8.1%
86498
8.2%
76528
8.2%
67459
9.4%
57966
10.0%
46112
7.7%
36613
8.3%

intake_year
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.436101
Minimum2013
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size622.6 KiB
2023-03-14T02:18:40.899992image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2013
5-th percentile2013
Q12014
median2015
Q32017
95-th percentile2017
Maximum2018
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.30315675
Coefficient of variation (CV)0.0006465879767
Kurtosis-0.9707684268
Mean2015.436101
Median Absolute Deviation (MAD)1
Skewness0.02591777362
Sum160573825
Variance1.698217516
MonotonicityNot monotonic
2023-03-14T02:18:41.072187image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
201518699
23.5%
201418645
23.4%
201617632
22.1%
201717440
21.9%
20134178
 
5.2%
20183078
 
3.9%
ValueCountFrequency (%)
20134178
 
5.2%
201418645
23.4%
201518699
23.5%
201617632
22.1%
201717440
21.9%
20183078
 
3.9%
ValueCountFrequency (%)
20183078
 
3.9%
201717440
21.9%
201617632
22.1%
201518699
23.5%
201418645
23.4%
20134178
 
5.2%

intake_monthyear
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct54
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size622.6 KiB
2015-06
 
2188
2015-05
 
2092
2016-05
 
2035
2014-05
 
1956
2014-07
 
1885
Other values (49)
69516 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters557704
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-12
2nd row2014-12
3rd row2014-03
4th row2014-04
5th row2013-11

Common Values

ValueCountFrequency (%)
2015-062188
 
2.7%
2015-052092
 
2.6%
2016-052035
 
2.6%
2014-051956
 
2.5%
2014-071885
 
2.4%
2017-051883
 
2.4%
2017-061843
 
2.3%
2014-061795
 
2.3%
2015-101738
 
2.2%
2015-081717
 
2.2%
Other values (44)60540
76.0%

Length

2023-03-14T02:18:41.245672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2015-062188
 
2.7%
2015-052092
 
2.6%
2016-052035
 
2.6%
2014-051956
 
2.5%
2014-071885
 
2.4%
2017-051883
 
2.4%
2017-061843
 
2.3%
2014-061795
 
2.3%
2015-101738
 
2.2%
2015-081717
 
2.2%
Other values (44)60540
76.0%

Most occurring characters

ValueCountFrequency (%)
0146648
26.3%
1112599
20.2%
291448
16.4%
-79672
14.3%
526665
 
4.8%
625091
 
4.5%
424757
 
4.4%
723968
 
4.3%
310791
 
1.9%
89576
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number478032
85.7%
Dash Punctuation79672
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0146648
30.7%
1112599
23.6%
291448
19.1%
526665
 
5.6%
625091
 
5.2%
424757
 
5.2%
723968
 
5.0%
310791
 
2.3%
89576
 
2.0%
96489
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
-79672
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common557704
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0146648
26.3%
1112599
20.2%
291448
16.4%
-79672
14.3%
526665
 
4.8%
625091
 
4.5%
424757
 
4.4%
723968
 
4.3%
310791
 
1.9%
89576
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII557704
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0146648
26.3%
1112599
20.2%
291448
16.4%
-79672
14.3%
526665
 
4.8%
625091
 
4.5%
424757
 
4.4%
723968
 
4.3%
310791
 
1.9%
89576
 
1.7%

intake_weekday
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size622.6 KiB
Saturday
12037 
Monday
11997 
Wednesday
11575 
Tuesday
11528 
Friday
11205 
Other values (2)
21330 

Length

Max length9
Median length8
Mean length7.158713224
Min length6

Characters and Unicode

Total characters570349
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowThursday
2nd rowFriday
3rd rowFriday
4th rowWednesday
5th rowSaturday

Common Values

ValueCountFrequency (%)
Saturday12037
15.1%
Monday11997
15.1%
Wednesday11575
14.5%
Tuesday11528
14.5%
Friday11205
14.1%
Thursday10995
13.8%
Sunday10335
13.0%

Length

2023-03-14T02:18:41.417430image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-03-14T02:18:41.624511image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
saturday12037
15.1%
monday11997
15.1%
wednesday11575
14.5%
tuesday11528
14.5%
friday11205
14.1%
thursday10995
13.8%
sunday10335
13.0%

Most occurring characters

ValueCountFrequency (%)
a91709
16.1%
d91247
16.0%
y79672
14.0%
u44895
7.9%
e34678
 
6.1%
r34237
 
6.0%
s34098
 
6.0%
n33907
 
5.9%
T22523
 
3.9%
S22372
 
3.9%
Other values (7)81011
14.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter490677
86.0%
Uppercase Letter79672
 
14.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a91709
18.7%
d91247
18.6%
y79672
16.2%
u44895
9.1%
e34678
 
7.1%
r34237
 
7.0%
s34098
 
6.9%
n33907
 
6.9%
t12037
 
2.5%
o11997
 
2.4%
Other values (2)22200
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
T22523
28.3%
S22372
28.1%
M11997
15.1%
W11575
14.5%
F11205
14.1%

Most occurring scripts

ValueCountFrequency (%)
Latin570349
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a91709
16.1%
d91247
16.0%
y79672
14.0%
u44895
7.9%
e34678
 
6.1%
r34237
 
6.0%
s34098
 
6.0%
n33907
 
5.9%
T22523
 
3.9%
S22372
 
3.9%
Other values (7)81011
14.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII570349
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a91709
16.1%
d91247
16.0%
y79672
14.0%
u44895
7.9%
e34678
 
6.1%
r34237
 
6.0%
s34098
 
6.0%
n33907
 
5.9%
T22523
 
3.9%
S22372
 
3.9%
Other values (7)81011
14.2%

intake_hour
Real number (ℝ≥0)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.48702179
Minimum0
Maximum23
Zeros299
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size622.6 KiB
2023-03-14T02:18:41.785902image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q111
median13
Q316
95-th percentile18
Maximum23
Range23
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.121172869
Coefficient of variation (CV)0.2314204661
Kurtosis1.573025261
Mean13.48702179
Median Absolute Deviation (MAD)2
Skewness-0.150852226
Sum1074538
Variance9.74172008
MonotonicityNot monotonic
2023-03-14T02:18:41.965168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1112931
16.2%
1210868
13.6%
139573
12.0%
148456
10.6%
168096
10.2%
158094
10.2%
175245
6.6%
184988
 
6.3%
103438
 
4.3%
91782
 
2.2%
Other values (14)6201
7.8%
ValueCountFrequency (%)
0299
 
0.4%
1129
 
0.2%
283
 
0.1%
367
 
0.1%
437
 
< 0.1%
522
 
< 0.1%
6151
 
0.2%
71253
1.6%
81420
1.8%
91782
2.2%
ValueCountFrequency (%)
23516
 
0.6%
22532
 
0.7%
21445
 
0.6%
20334
 
0.4%
19913
 
1.1%
184988
6.3%
175245
6.6%
168096
10.2%
158094
10.2%
148456
10.6%

intake_number
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.126819962
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size622.6 KiB
2023-03-14T02:18:42.131105image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum13
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4560570024
Coefficient of variation (CV)0.4047292539
Kurtosis57.63880095
Mean1.126819962
Median Absolute Deviation (MAD)0
Skewness5.785008652
Sum89776
Variance0.2079879894
MonotonicityNot monotonic
2023-03-14T02:18:42.315586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
171993
90.4%
26052
 
7.6%
31152
 
1.4%
4302
 
0.4%
5101
 
0.1%
639
 
< 0.1%
715
 
< 0.1%
87
 
< 0.1%
103
 
< 0.1%
113
 
< 0.1%
Other values (3)5
 
< 0.1%
ValueCountFrequency (%)
171993
90.4%
26052
 
7.6%
31152
 
1.4%
4302
 
0.4%
5101
 
0.1%
639
 
< 0.1%
715
 
< 0.1%
87
 
< 0.1%
93
 
< 0.1%
103
 
< 0.1%
ValueCountFrequency (%)
131
 
< 0.1%
121
 
< 0.1%
113
 
< 0.1%
103
 
< 0.1%
93
 
< 0.1%
87
 
< 0.1%
715
 
< 0.1%
639
 
< 0.1%
5101
 
0.1%
4302
0.4%

time_in_shelter
Categorical

HIGH CARDINALITY

Distinct29319
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Memory size622.6 KiB
0 days 00:14:00.000000000
 
77
0 days 00:12:00.000000000
 
77
0 days 00:06:00.000000000
 
77
0 days 00:17:00.000000000
 
77
0 days 00:11:00.000000000
 
77
Other values (29314)
79287 

Length

Max length28
Median length25
Mean length25.33792298
Min length25

Characters and Unicode

Total characters2018723
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17362 ?
Unique (%)21.8%

Sample

1st row0 days 14:07:00.000000000
2nd row1 days 06:14:00.000000000
3rd row1 days 02:44:00.000000000
4th row4 days 23:17:00.000000000
5th row0 days 02:52:00.000000000

Common Values

ValueCountFrequency (%)
0 days 00:14:00.00000000077
 
0.1%
0 days 00:12:00.00000000077
 
0.1%
0 days 00:06:00.00000000077
 
0.1%
0 days 00:17:00.00000000077
 
0.1%
0 days 00:11:00.00000000077
 
0.1%
0 days 00:13:00.00000000076
 
0.1%
0 days 00:25:00.00000000075
 
0.1%
0 days 00:21:00.00000000075
 
0.1%
0 days 00:08:00.00000000074
 
0.1%
0 days 00:19:00.00000000072
 
0.1%
Other values (29309)78915
99.0%

Length

2023-03-14T02:18:42.513542image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
days79672
33.3%
018496
 
7.7%
48198
 
3.4%
15250
 
2.2%
54800
 
2.0%
34272
 
1.8%
23771
 
1.6%
63335
 
1.4%
72605
 
1.1%
82243
 
0.9%
Other values (1897)106374
44.5%

Most occurring characters

ValueCountFrequency (%)
0985454
48.8%
:159344
 
7.9%
159344
 
7.9%
d79672
 
3.9%
a79672
 
3.9%
y79672
 
3.9%
s79672
 
3.9%
.79672
 
3.9%
165108
 
3.2%
261926
 
3.1%
Other values (7)189187
 
9.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1301675
64.5%
Lowercase Letter318688
 
15.8%
Other Punctuation239016
 
11.8%
Space Separator159344
 
7.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0985454
75.7%
165108
 
5.0%
261926
 
4.8%
343432
 
3.3%
440405
 
3.1%
535731
 
2.7%
619699
 
1.5%
717809
 
1.4%
816208
 
1.2%
915903
 
1.2%
Lowercase Letter
ValueCountFrequency (%)
d79672
25.0%
a79672
25.0%
y79672
25.0%
s79672
25.0%
Other Punctuation
ValueCountFrequency (%)
:159344
66.7%
.79672
33.3%
Space Separator
ValueCountFrequency (%)
159344
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1700035
84.2%
Latin318688
 
15.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0985454
58.0%
:159344
 
9.4%
159344
 
9.4%
.79672
 
4.7%
165108
 
3.8%
261926
 
3.6%
343432
 
2.6%
440405
 
2.4%
535731
 
2.1%
619699
 
1.2%
Other values (3)49920
 
2.9%
Latin
ValueCountFrequency (%)
d79672
25.0%
a79672
25.0%
y79672
25.0%
s79672
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2018723
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0985454
48.8%
:159344
 
7.9%
159344
 
7.9%
d79672
 
3.9%
a79672
 
3.9%
y79672
 
3.9%
s79672
 
3.9%
.79672
 
3.9%
165108
 
3.2%
261926
 
3.1%
Other values (7)189187
 
9.4%

time_in_shelter_days
Real number (ℝ≥0)

Distinct29319
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.75711555
Minimum0
Maximum1606.194444
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size622.6 KiB
2023-03-14T02:18:42.708786image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04930555556
Q11.102083333
median4.987152778
Q313.61076389
95-th percentile71.10881944
Maximum1606.194444
Range1606.194444
Interquartile range (IQR)12.50868056

Descriptive statistics

Standard deviation41.67935938
Coefficient of variation (CV)2.487263352
Kurtosis208.0111158
Mean16.75711555
Median Absolute Deviation (MAD)4.263541667
Skewness10.52498995
Sum1335072.91
Variance1737.168998
MonotonicityNot monotonic
2023-03-14T02:18:42.936102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00972222222277
 
0.1%
0.00833333333377
 
0.1%
0.00416666666777
 
0.1%
0.0118055555677
 
0.1%
0.00763888888977
 
0.1%
0.00902777777876
 
0.1%
0.0173611111175
 
0.1%
0.0145833333375
 
0.1%
0.00555555555674
 
0.1%
0.0131944444472
 
0.1%
Other values (29309)78915
99.0%
ValueCountFrequency (%)
01
 
< 0.1%
0.000694444444431
< 0.1%
0.00138888888937
< 0.1%
0.00208333333347
0.1%
0.00277777777851
0.1%
0.00347222222270
0.1%
0.00416666666777
0.1%
0.00486111111156
0.1%
0.00555555555674
0.1%
0.0062560
0.1%
ValueCountFrequency (%)
1606.1944441
< 0.1%
1478.21
< 0.1%
1411.1479171
< 0.1%
1268.9751
< 0.1%
1268.8729171
< 0.1%
1255.0472221
< 0.1%
1206.8465281
< 0.1%
1157.9416671
< 0.1%
1101.7840281
< 0.1%
1067.8298611
< 0.1%

Interactions

2023-03-14T02:18:17.208969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:32.517106image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:35.316377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:38.633217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:42.302608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:45.131166image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:47.879142image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:51.371427image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:54.939180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:57.783588image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:00.933161image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:03.927402image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:07.690812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:10.484139image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:13.793275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:17.486690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:32.702395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:35.493555image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:38.947053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:42.485583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:45.306468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:48.056745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:51.647439image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:55.118985image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:57.968738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:01.120523image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:04.200580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:07.867113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:10.669244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:13.982711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:17.785476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:32.899075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:35.671933image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:39.238199image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:42.687120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:45.489372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:48.253959image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:51.933569image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:55.304460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:58.156406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:01.304439image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:04.486785image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:08.047763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:10.875017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:14.175457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:18.091527image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:33.080448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:35.860922image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:39.516102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:42.872150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:45.676288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:48.441379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:52.201071image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:55.503578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:58.334485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:01.503596image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:04.768103image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:08.235130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:11.052934image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:14.357598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:18.715932image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:33.266857image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:36.052994image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:39.807893image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:43.072854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:45.856009image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:48.632794image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:52.523144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:55.691957image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:58.528004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:01.703442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:05.048389image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:08.418198image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:11.244295image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:14.569867image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:19.008781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:33.444615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:36.232571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:40.095064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:43.250311image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:46.031747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:48.824543image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:52.804904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:55.879925image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:58.699651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:01.888688image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:05.326018image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:08.604501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:11.416919image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:14.754908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:19.694659image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:33.624293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:36.420400image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:40.339454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:43.435675image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:46.222760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:49.294236image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:53.115771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:56.063882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:58.883126image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:02.073366image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:05.601772image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:08.799807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:11.606505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:15.348256image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:20.012698image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:33.818776image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:36.599116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:40.777266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:43.615601image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:46.411211image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:49.474179image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:53.391673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:56.251828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:59.071115image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:02.255998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:05.910936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:08.991438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:11.789778image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:15.533812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:20.459417image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:34.016776image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:36.787263image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:40.978391image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:43.798334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:46.595408image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:49.661778image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:53.610006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:56.448976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:59.269673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:02.442113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:06.193531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:09.175324image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:11.975219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:15.713647image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:20.648735image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:34.194442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-03-14T02:17:41.164427image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-03-14T02:17:50.248121image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:54.180154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:57.027199image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:59.849278image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:03.020116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:06.958597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:09.720147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:12.536829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:16.285627image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:21.286422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:34.753174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:37.771796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:41.719865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:44.539318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:47.331977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:50.516442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:54.385074image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:57.212137image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:00.035090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:03.208283image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:07.142617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:09.916351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:13.209589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:16.477524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:21.563050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:34.933184image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:38.067869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:41.896922image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:44.718628image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:47.503557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:50.797639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:54.560492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:57.402434image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:00.214338image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:03.393853image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:07.320235image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:10.095858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:13.419640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:16.652339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:21.879567image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:35.119423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:38.330831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:42.084095image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:44.903106image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:47.680149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:51.062006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:54.743804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:17:57.586207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:00.393217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:03.623306image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:07.496319image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:10.278480image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:13.598193image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-14T02:18:16.878375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-03-14T02:18:43.130143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-03-14T02:18:43.405869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-03-14T02:18:43.758152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-03-14T02:18:44.212994image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-03-14T02:18:44.647637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-03-14T02:18:23.074774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-14T02:18:25.296419image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-14T02:18:26.415367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2023-03-14T02:18:26.930352image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

age_upon_outcomeanimal_id_outcomedate_of_birthoutcome_subtypeoutcome_typesex_upon_outcomeage_upon_outcome_(days)age_upon_outcome_(years)age_upon_outcome_age_groupoutcome_datetimeoutcome_monthoutcome_yearoutcome_monthyearoutcome_weekdayoutcome_houroutcome_numberdob_yeardob_monthdob_monthyearage_upon_intakeanimal_id_intakeanimal_typebreedcolorfound_locationintake_conditionintake_typesex_upon_intakecountage_upon_intake_(days)age_upon_intake_(years)age_upon_intake_age_groupintake_datetimeintake_monthintake_yearintake_monthyearintake_weekdayintake_hourintake_numbertime_in_sheltertime_in_shelter_days
010 yearsA0061002007-07-09 00:00:00NaNReturn to OwnerNeutered Male365010.0(7.5, 10.0]2017-12-07 14:07:001220172017-12Thursday01.0200772017-1210 yearsA006100DogSpinone Italiano MixYellow/WhiteColony Creek And Hunters Trace in Austin (TX)NormalStrayNeutered Male1365010.0(7.5, 10.0]2017-12-07 00:00:001220172017-12Thursday141.00 days 14:07:00.0000000000.588194
17 yearsA0061002007-07-09 00:00:00NaNReturn to OwnerNeutered Male25557.0(5.0, 7.5]2014-12-20 16:35:001220142014-12Saturday162.0200772014-127 yearsA006100DogSpinone Italiano MixYellow/White8700 Research Blvd in Austin (TX)NormalPublic AssistNeutered Male125557.0(5.0, 7.5]2014-12-19 10:21:001220142014-12Friday102.01 days 06:14:00.0000000001.259722
26 yearsA0061002007-07-09 00:00:00NaNReturn to OwnerNeutered Male21906.0(5.0, 7.5]2014-03-08 17:10:00320142014-03Saturday173.0200772014-036 yearsA006100DogSpinone Italiano MixYellow/White8700 Research in Austin (TX)NormalPublic AssistNeutered Male121906.0(5.0, 7.5]2014-03-07 14:26:00320142014-03Friday143.01 days 02:44:00.0000000001.113889
310 yearsA0477592004-04-02 00:00:00PartnerTransferNeutered Male365010.0(7.5, 10.0]2014-04-07 15:12:00420142014-04Monday151.0200442014-0410 yearsA047759DogDachshundTricolorAustin (TX)NormalOwner SurrenderNeutered Male1365010.0(7.5, 10.0]2014-04-02 15:55:00420142014-04Wednesday151.04 days 23:17:00.0000000004.970139
416 yearsA1340671997-10-16 00:00:00NaNReturn to OwnerNeutered Male584016.0(15.0, 17.5]2013-11-16 11:54:001120132013-11Saturday111.01997102013-1116 yearsA134067DogShetland SheepdogBrown/White12034 Research Blvd in Austin (TX)InjuredPublic AssistNeutered Male1584016.0(15.0, 17.5]2013-11-16 09:02:001120132013-11Saturday91.00 days 02:52:00.0000000000.119444
515 yearsA1411421998-06-01 00:00:00NaNReturn to OwnerSpayed Female547515.0(12.5, 15.0]2013-11-17 11:40:001120132013-11Sunday111.0199862013-1115 yearsA141142DogLabrador Retriever/Pit BullBlack/WhiteAustin (TX)AgedStraySpayed Female1547515.0(12.5, 15.0]2013-11-16 14:46:001120132013-11Saturday141.00 days 20:54:00.0000000000.870833
615 yearsA1634591999-10-19 00:00:00NaNReturn to OwnerIntact Female547515.0(12.5, 15.0]2014-11-14 19:28:001120142014-11Friday191.01999102014-1115 yearsA163459DogMiniature Schnauzer MixBlack/GrayIh 35 And 41St St in Austin (TX)NormalStrayIntact Female1547515.0(12.5, 15.0]2014-11-14 15:11:001120142014-11Friday151.00 days 04:17:00.0000000000.178472
715 yearsA1657521999-08-18 00:00:00NaNReturn to OwnerNeutered Male547515.0(12.5, 15.0]2014-09-15 16:35:00920142014-09Monday161.0199982014-0915 yearsA165752DogLhasa Apso MixBrown/WhiteGatlin Gun Rd And Brodie in Austin (TX)NormalStrayNeutered Male1547515.0(12.5, 15.0]2014-09-15 11:28:00920142014-09Monday111.00 days 05:07:00.0000000000.213194
815 yearsA1785691999-03-17 00:00:00NaNReturn to OwnerNeutered Male547515.0(12.5, 15.0]2014-03-23 15:57:00320142014-03Sunday151.0199932014-0315 yearsA178569DogShetland Sheepdog MixWhite/BlackAustin (TX)NormalPublic AssistNeutered Male1547515.0(12.5, 15.0]2014-03-17 09:45:00320142014-03Monday91.06 days 06:12:00.0000000006.258333
918 yearsA1895921997-08-01 00:00:00NaNReturn to OwnerSpayed Female657018.0(17.5, 20.0]2015-09-18 19:04:00920152015-09Friday191.0199782015-0918 yearsA189592DogShetland Sheepdog MixBrown/WhiteChesney And Slaughter in Austin (TX)NormalStraySpayed Female1657018.0(17.5, 20.0]2015-09-18 17:46:00920152015-09Friday171.00 days 01:18:00.0000000000.054167

Last rows

age_upon_outcomeanimal_id_outcomedate_of_birthoutcome_subtypeoutcome_typesex_upon_outcomeage_upon_outcome_(days)age_upon_outcome_(years)age_upon_outcome_age_groupoutcome_datetimeoutcome_monthoutcome_yearoutcome_monthyearoutcome_weekdayoutcome_houroutcome_numberdob_yeardob_monthdob_monthyearage_upon_intakeanimal_id_intakeanimal_typebreedcolorfound_locationintake_conditionintake_typesex_upon_intakecountage_upon_intake_(days)age_upon_intake_(years)age_upon_intake_age_groupintake_datetimeintake_monthintake_yearintake_monthyearintake_weekdayintake_hourintake_numbertime_in_sheltertime_in_shelter_days
796622 weeksA7690552018-03-14 00:00:00PartnerTransferUnknown140.038356(-0.025, 2.5]2018-03-29 18:12:00320182018-03Thursday181.0201832018-032 weeksA769055CatDomestic Shorthair MixCream1208 Coaches Crossing in Pflugerville (TX)NormalStrayUnknown1140.038356(-0.025, 2.5]2018-03-29 16:23:00320182018-03Thursday161.00 days 01:49:00.0000000000.075694
796632 weeksA7690562018-03-14 00:00:00PartnerTransferUnknown140.038356(-0.025, 2.5]2018-03-29 18:13:00320182018-03Thursday181.0201832018-032 weeksA769056CatDomestic Shorthair MixBlack1208 Coaches Crossing in Pflugerville (TX)NormalStrayUnknown1140.038356(-0.025, 2.5]2018-03-29 16:23:00320182018-03Thursday161.00 days 01:50:00.0000000000.076389
796642 weeksA7690572018-03-14 00:00:00PartnerTransferUnknown140.038356(-0.025, 2.5]2018-03-29 18:13:00320182018-03Thursday181.0201832018-032 weeksA769057CatDomestic Shorthair MixGray Tabby1208 Coaches Crossing in Pflugerville (TX)NormalStrayUnknown1140.038356(-0.025, 2.5]2018-03-29 16:23:00320182018-03Thursday161.00 days 01:50:00.0000000000.076389
796652 weeksA7690582018-03-14 00:00:00PartnerTransferUnknown140.038356(-0.025, 2.5]2018-03-29 18:13:00320182018-03Thursday181.0201832018-032 weeksA769058CatDomestic Shorthair MixBlue1208 Coaches Crossing in Pflugerville (TX)NormalStrayUnknown1140.038356(-0.025, 2.5]2018-03-29 16:23:00320182018-03Thursday161.00 days 01:50:00.0000000000.076389
796662 weeksA7690592018-03-14 00:00:00PartnerTransferUnknown140.038356(-0.025, 2.5]2018-03-30 09:23:00320182018-03Friday91.0201832018-032 weeksA769059CatDomestic Shorthair MixBlue1208 Coaches Crossing in Pflugerville (TX)NormalStrayUnknown1140.038356(-0.025, 2.5]2018-03-29 16:23:00320182018-03Thursday161.00 days 17:00:00.0000000000.708333
796672 weeksA7690602018-03-14 00:00:00PartnerTransferUnknown140.038356(-0.025, 2.5]2018-03-29 18:14:00320182018-03Thursday181.0201832018-032 weeksA769060CatDomestic Shorthair MixTortie1208 Coaches Crossing in Pflugerville (TX)NormalStrayUnknown1140.038356(-0.025, 2.5]2018-03-29 16:23:00320182018-03Thursday161.00 days 01:51:00.0000000000.077083
796682 yearsA7690642016-03-29 00:00:00Rabies RiskEuthanasiaUnknown7302.000000(-0.025, 2.5]2018-03-29 18:30:00320182018-03Thursday181.0201632018-032 yearsA769064OtherBat MixBrown2519 Scarbrough Dr in Travis (TX)NormalWildlifeUnknown17302.000000(-0.025, 2.5]2018-03-29 17:13:00320182018-03Thursday171.00 days 01:17:00.0000000000.053472
796691 yearA7690652017-03-29 00:00:00Rabies RiskEuthanasiaUnknown3651.000000(-0.025, 2.5]2018-03-29 18:28:00320182018-03Thursday181.0201732018-031 yearA769065OtherBat MixBrown1122 Walton Ln in Austin (TX)NormalWildlifeUnknown13651.000000(-0.025, 2.5]2018-03-29 17:19:00320182018-03Thursday171.00 days 01:09:00.0000000000.047917
7967010 monthsA7690662017-05-01 00:00:00NaNReturn to OwnerIntact Male3000.821918(-0.025, 2.5]2018-03-31 12:37:00320182018-03Saturday121.0201752018-035 monthsA769066DogLabrador Retriever MixBlack/White12034 Research Blvd Sb in Austin (TX)NormalStrayIntact Male11500.410959(-0.025, 2.5]2018-03-29 18:19:00320182018-03Thursday181.01 days 18:18:00.0000000001.762500
7967110 yearsA7690672008-03-29 00:00:00SufferingEuthanasiaSpayed Female365010.000000(7.5, 10.0]2018-03-30 13:52:00320182018-03Friday131.0200832018-0310 yearsA769067DogBoxer MixBrown Brindle11216 Jollyville Rd 216 in Austin (TX)AgedStraySpayed Female1365010.000000(7.5, 10.0]2018-03-29 18:20:00320182018-03Thursday181.00 days 19:32:00.0000000000.813889

Duplicate rows

Most frequently occurring

age_upon_outcomeanimal_id_outcomedate_of_birthoutcome_subtypeoutcome_typesex_upon_outcomeage_upon_outcome_(days)age_upon_outcome_(years)age_upon_outcome_age_groupoutcome_datetimeoutcome_monthoutcome_yearoutcome_monthyearoutcome_weekdayoutcome_houroutcome_numberdob_yeardob_monthdob_monthyearage_upon_intakeanimal_id_intakeanimal_typebreedcolorfound_locationintake_conditionintake_typesex_upon_intakecountage_upon_intake_(days)age_upon_intake_(years)age_upon_intake_age_groupintake_datetimeintake_monthintake_yearintake_monthyearintake_weekdayintake_hourintake_numbertime_in_sheltertime_in_shelter_days# duplicates
710 monthsA6957982014-02-23 00:00:00SufferingEuthanasiaNeutered Male3000.821918(-0.025, 2.5]2015-01-23 12:34:00120152015-01Friday121.0201422015-0110 monthsA695798CatDomestic Shorthair MixOrangeAustin (TX)SickOwner SurrenderNeutered Male13000.821918(-0.025, 2.5]2015-01-23 11:48:00120152015-01Friday111.00 days 00:46:00.0000000000.0319444
810 monthsA7619362017-01-12 00:00:00PartnerTransferIntact Male3000.821918(-0.025, 2.5]2017-11-16 12:54:001120172017-11Thursday121.0201712017-119 monthsA761936DogScottish Terrier MixBrown Brindle8400 Old Bee Caves in Austin (TX)NormalStrayIntact Male12700.739726(-0.025, 2.5]2017-11-12 11:16:001120172017-11Sunday111.04 days 01:38:00.0000000004.0680564
01 monthA7509632017-05-04 00:00:00FosterAdoptionNeutered Male300.082192(-0.025, 2.5]2018-03-27 11:33:00720172017-07Saturday181.0201752017-0710 monthsA750963CatDomestic Medium Hair MixBlueAustin (TX)NormalOwner SurrenderNeutered Male13000.821918(-0.025, 2.5]2017-07-01 18:37:00320182018-03Tuesday111.0268 days 16:56:00.000000000268.7055562
11 weeksA7687552018-03-21 00:00:00PartnerTransferUnknown70.019178(-0.025, 2.5]2018-03-29 15:57:00320182018-03Thursday151.0201832018-033 daysA768755CatDomestic Shorthair MixBrown Tabby1406 Sahara Avenue in Austin (TX)NursingStrayUnknown130.008219(-0.025, 2.5]2018-03-24 15:50:00320182018-03Saturday151.05 days 00:07:00.0000000005.0048612
21 yearA6814462013-06-16 00:00:00Rabies RiskEuthanasiaUnknown3651.000000(-0.025, 2.5]2014-06-16 17:14:00620142014-06Monday171.0201362014-061 yearA681446OtherBatBrown6208 Tupelo Dr in Austin (TX)NormalWildlifeUnknown13651.000000(-0.025, 2.5]2014-06-16 16:38:00620142014-06Monday161.00 days 00:36:00.0000000000.0250002
31 yearA6827812013-01-02 00:00:00SCRPTransferNeutered Male3651.000000(-0.025, 2.5]2014-08-13 18:41:00720142014-07Thursday91.0201312014-071 yearA682781CatDomestic Shorthair MixWhite/BlackColorado & 6Th Street in Austin (TX)NormalStrayNeutered Male13651.000000(-0.025, 2.5]2014-07-03 09:00:00820142014-08Wednesday181.041 days 09:41:00.00000000041.4034722
41 yearA6988642014-03-19 00:00:00Rabies RiskEuthanasiaUnknown3651.000000(-0.025, 2.5]2015-03-22 10:43:00320152015-03Sunday101.0201432015-031 yearA698864OtherSkunk MixBlack12129 Glass Rd in Travis (TX)InjuredWildlifeUnknown13651.000000(-0.025, 2.5]2015-03-19 13:37:00320152015-03Thursday131.02 days 21:06:00.0000000002.8791672
51 yearA7687542017-03-24 00:00:00SnrTransferIntact Female3651.000000(-0.025, 2.5]2018-03-31 19:26:00320182018-03Saturday191.0201732018-031 yearA768754CatDomestic Shorthair MixBlack1406 Sahara Avenue in Austin (TX)NursingStrayIntact Female13651.000000(-0.025, 2.5]2018-03-24 15:50:00320182018-03Saturday151.07 days 03:36:00.0000000007.1500002
61 yearA7689172017-03-27 00:00:00Rabies RiskEuthanasiaUnknown3651.000000(-0.025, 2.5]2018-03-27 14:23:00320182018-03Tuesday141.0201732018-031 yearA768917OtherBat MixBrown1900 Simond Avenue in Austin (TX)NormalWildlifeUnknown13651.000000(-0.025, 2.5]2018-03-27 12:32:00320182018-03Tuesday121.00 days 01:51:00.0000000000.0770832
92 yearsA6868272012-02-27 00:00:00SCRPTransferSpayed Female7302.000000(-0.025, 2.5]2014-09-01 12:12:00820142014-08Thursday91.0201222014-082 yearsA686827CatDomestic Shorthair MixCalico1016 Camino La Costa in Austin (TX)NormalStraySpayed Female17302.000000(-0.025, 2.5]2014-08-28 09:00:00920142014-09Monday121.04 days 03:12:00.0000000004.1333332